Methods and systems for self-fulfillment of an alimentary instruction set based on vibrant constitutional guidance

ABSTRACT

A system for self-fulfillment of an alimentary instruction set based on vibrant constitutional guidance using artificial intelligence. The system includes a computing device designed and configured to receive training data. The computing device is further configured to record at least a biological extraction from a user and generate a diagnostic output. The computing device is further configured to generate a self-fulfillment instruction set utilizing the diagnostic output. The computing device is further configured to receive a user entry containing a completed alimentary self-fulfillment action. The computing device is further configured to update the self-fulfillment instruction set as a function of an alimentary self-fulfillment action.

RELATED APPLICATION DATA

This application is a continuation-in-part of Ser. No. 16/729,330 filedon Dec. 28, 2019 and entitled “SYSTEMS AND METHODS FOR GENERATINGALIMENTARY INSTRUCTION SETS BASED ON VIBRANT CONSTITUTIONAL GUIDANCE,”which is a continuation of U.S. patent application Ser. No. 16/375,303,filed on Apr. 4, 2019 and entitled “SYSTEMS AND METHODS FOR GENERATINGALIMENTARY INSTRUCTION SETS BASED ON VIBRANT CONSTITUTIONAL GUIDANCE,”which is hereby incorporated by reference herein in its entirety.

FIELD OF THE INVENTION

The present invention generally relates to the field of artificialintelligence. In particular, the present invention is directed tomethods and systems for self-fulfillment of an alimentary instructionset.

BACKGROUND

Generating alimentary instructions is a complex process hampered by thecomplexity and amount of data involved. Effective and accurate analysisof data to produce practical and useful instruction sets is challenging.Current solutions fail to account for the multivariate complexity inproducing meaningful instruction sets.

SUMMARY OF THE DISCLOSURE

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

In an aspect, a system for self-fulfillment of an alimentary instructionset based on vibrant constitutional guidance. The system includes acomputing device, the computing device including a diagnostic engineoperating on the computing device, the diagnostic engine configured toreceive training data, wherein receiving the training data furthercomprises receiving a first training set including a plurality of firstdata entries, each first data entry of the plurality of first dataentries including at least an element of physiological state data and atleast a correlated first prognostic label; and receiving a secondtraining set including a plurality of second data entries, each seconddata entry of the plurality of second data entries including at least asecond prognostic label and at least a correlated ameliorative processlabel. The computing device is further configured to record at least abiological extraction from a user wherein the at least a biologicalextraction contains at least an element of physiological state data. Thecomputing device is further configured to generate a diagnostic outputbased on the at least a biological extraction and the training data,wherein generating further comprises performing at least amachine-learning algorithm as a function of the training data and the atleast a biological extraction. The computing device includes afulfillment module operating on the at least a server the fulfillmentmodule designed and configured to generate a self-fulfillmentinstruction set utilizing the diagnostic output wherein theself-fulfillment instruction set identifies a self-fulfillment action.The computing device is further configured to receive at least a userentry containing a completed alimentary self-fulfillment action. Thecomputing device is further configured update the self-fulfillmentinstruction set as a function of the alimentary self-fulfillment action.

In an aspect, a method of self-fulfillment of an alimentary instructionset based on vibrant constitutional guidance. The method includesreceiving by a computing device training data, wherein receiving thetraining data further comprises receiving a first training set includinga plurality of first data entries, each first data entry of theplurality of first data entries including at least an element ofphysiological state data and at least a correlated first prognosticlabel; and receiving a second training set including a plurality ofsecond data entries, each second data entry of the plurality of seconddata entries including at least a second prognostic label and at least acorrelated ameliorative process label. The method includes recording bythe computing device at least a biological extraction from a userwherein the at least a biological extraction contains at least anelement of physiological state data. The method includes generating bythe computing device a diagnostic output based on the at least abiological extraction and the training data, wherein generating furthercomprises performing at least a machine-learning algorithm as a functionof the training data and the at least a biological extraction. Themethod includes generating by the computing device a self-fulfillmentinstruction set utilizing the diagnostic output wherein theself-fulfillment instruction set identifies a self-fulfillment action.The method includes receiving by the computing device at least a userentry containing a completed alimentary self-fulfillment action. Themethod includes updating by the computing device the self-fulfillmentinstruction set as a function of the alimentary self-fulfillment action.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram illustrating an exemplary embodiment of asystem for self-fulfillment of an alimentary instruction set based onvibrant constitutional guidance;

FIG. 2 is a block diagram illustrating embodiments of data storagefacilities for use in disclosed systems and methods;

FIG. 3 is a block diagram illustrating an exemplary embodiment of abiological extraction database;

FIG. 4 is a block diagram illustrating an exemplary embodiment of anexpert knowledge database;

FIG. 5 is a block diagram illustrating an exemplary embodiment of aprognostic label database;

FIG. 6 is a block diagram illustrating an exemplary embodiment of anameliorative process label database;

FIG. 7 is a block diagram illustrating an exemplary embodiment of aprognostic label learner and associated system elements;

FIG. 8 is a block diagram illustrating an exemplary embodiment of anameliorative process label learner and associated system elements;

FIG. 9 is a block diagram illustrating an exemplary embodiment of analimentary instruction label learner and associated system elements;

FIG. 10 is a block diagram illustrating an exemplary embodiment of aplan generator module and associated system elements;

FIG. 11 is a block diagram illustrating an exemplary embodiment of aprognostic label classification database;

FIG. 12 is a block diagram illustrating an exemplary embodiment of anameliorative process label classification database;

FIG. 13 is a block diagram illustrating an exemplary embodiment of anarrative language database;

FIG. 14 is a block diagram illustrating an exemplary embodiment of animage database;

FIG. 15 is a block diagram illustrating an exemplary embodiment of auser database;

FIG. 16 is a block diagram illustrating an exemplary embodiment of analimentary instruction set module and associated system elements;

FIG. 17 is a block diagram illustrating an exemplary embodiment of analimentary instruction label classification database;

FIG. 18 is a block diagram illustrating an exemplary embodiment of aself-fulfillment learner and associated system elements;

FIG. 19 is a block diagram illustrating an exemplary embodiment of avariables database;

FIG. 20 is a block diagram illustrating an exemplary embodiment of afulfillment module and associated system elements;

FIG. 21 is a block diagram illustrating an exemplary embodiment of afulfillment database;

FIG. 22 is a block diagram illustrating an exemplary embodiment of amatching database;

FIG. 23 is a block diagram illustrating an exemplary embodiment of anadvisory module and associated system elements;

FIG. 24 is a block diagram illustrating an exemplary embodiment of anartificial intelligence advisor and associated system elements;

FIG. 25 is a block diagram illustrating an exemplary embodiment of anadvisory database;

FIG. 26 is a block diagram illustrating an exemplary embodiment of adefault response database;

FIG. 27 is a flow diagram illustrating an exemplary embodiment of amethod of self-fulfillment of an alimentary instruction set based onvibrant constitutional guidance; and

FIG. 28 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed tomethods and systems for self-fulfillment of an alimentary instructionset based on vibrant constitutional guidance. In an embodiment, methodsand systems are provided for self-fulfillment of an alimentaryinstruction set. User entries containing actions a user engaged in toself-fulfill an alimentary instruction set may be matched and utilizedto update alimentary instruction sets. Alimentary instruction sets maybe generated from biological extractions received from a user.Alimentary instruction sets may be updated based on machine learningprocesses including both supersized and unsupervised processes.

Turning now to FIG. 1, a system 100 for self-fulfillment of analimentary instruction set based on vibrant constitutional guidance isillustrated. System 100 includes a computing device 102. A computingdevice 102 may include any computing device as described herein,including without limitation a microcontroller, microprocessor, digitalsignal processor (DSP) and/or system on a chip (SoC) as described hereinA computing device 102 may be housed with, may be incorporated in, ormay incorporate one or more sensors of at least a sensor. Computingdevice may include, be included in, and/or communicate with a mobiledevice such as a mobile telephone or smartphone. A computing device 102may include a single computing device operating independently, or mayinclude two or more computing device operating in concert, in parallel,sequentially or the like; two or more computing devices may be includedtogether in a single computing device or in two or more computingdevices. A computing device 102 with one or more additional devices asdescribed below in further detail via a network interface device.Network interface device may be utilized for connecting a computingdevice 102 to one or more of a variety of networks, and one or moredevices. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network may employ a wiredand/or a wireless mode of communication. In general, any networktopology may be used. Information (e.g., data, software etc.) may becommunicated to and/or from a computer and/or a computing device. Acomputing device 102 may include but is not limited to, for example, acomputing device 102 or cluster of computing devices in a first locationand a second computing device or cluster of computing devices in asecond location. A computing device 102 may include one or morecomputing devices dedicated to data storage, security, distribution oftraffic for load balancing, and the like. A computing device 102 maydistribute one or more computing tasks as described below across aplurality of computing devices of computing device, which may operate inparallel, in series, redundantly, or in any other manner used fordistribution of tasks or memory between computing devices. A computingdevice 102 may be implemented using a “shared nothing” architecture inwhich data is cached at the worker, in an embodiment, this may enablescalability of system 100 and/or computing device.

Still referring to FIG. 1, system 100 includes a diagnostic engine 104operating on the a computing device 102, wherein the diagnostic engine104 configured to receive a first training set including a plurality offirst data entries, each first data entry of the plurality of first dataentries including at least an element of physiological state data and atleast a correlated first prognostic label; receive a second training setincluding a plurality of second data entries, each second data entry ofthe plurality of second data entries including at least a secondprognostic label and at least a correlated ameliorative process label;receive at least a biological extraction from a user; and generate adiagnostic output based on the at least a biological extraction, thediagnostic output including at least a prognostic label and at least anameliorative process label using the first training set, the secondtraining set, and the at least a biological extraction. A computingdevice 102, diagnostic engine 104, and/or one or more modules operatingthereon may be designed and/or configured to perform any method, methodstep, or sequence of method steps in any embodiment described in thisdisclosure, in any order and with any degree of repetition. Forinstance, a computing device 102 and/or diagnostic engine 104 may beconfigured to perform a single step or sequence repeatedly until adesired or commanded outcome is achieved; repetition of a step or asequence of steps may be performed iteratively and/or recursively usingoutputs of previous repetitions as inputs to subsequent repetitions,aggregating inputs and/or outputs of repetitions to produce an aggregateresult, reduction or decrement of one or more variables such as globalvariables, and/or division of a larger processing task into a set ofiteratively addressed smaller processing tasks. A computing device 102and/or diagnostic engine 104 may perform any step or sequence of stepsas described in this disclosure in parallel, such as simultaneouslyand/or substantially simultaneously performing a step two or more timesusing two or more parallel threads, processor cores, or the like;division of tasks between parallel threads and/or processes may beperformed according to any protocol suitable for division of tasksbetween iterations. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in whichsteps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

Continuing to refer to FIG. 1, diagnostic engine 104 may be designed andconfigured to receive training data. Training data, as used herein, isdata containing correlation that a machine-learning process may use tomodel relationships between two or more categories of data elements. Forinstance, and without limitation, training data may include a pluralityof data entries, each entry representing a set of data elements thatwere recorded, received, and/or generated together; data elements may becorrelated by shared existence in a given data entry, by proximity in agiven data entry, or the like. Multiple data entries in training datamay evince one or more trends in correlations between categories of dataelements; for instance, and without limitation, a higher value of afirst data element belonging to a first category of data element maytend to correlate to a higher value of a second data element belongingto a second category of data element, indicating a possible proportionalor other mathematical relationship linking values belonging to the twocategories. Multiple categories of data elements may be related intraining data according to various correlations; correlations mayindicate causative and/or predictive links between categories of dataelements, which may be modeled as relationships such as mathematicalrelationships by machine-learning processes as described in furtherdetail below. Training data may be formatted and/or organized bycategories of data elements, for instance by associating data elementswith one or more descriptors corresponding to categories of dataelements. As a non-limiting example, training data may include dataentered in standardized forms by persons or processes, such that entryof a given data element in a given field in a form may be mapped to oneor more descriptors of categories. Elements in training data may belinked to descriptors of categories by tags, tokens, or other dataelements; for instance, and without limitation, training data may beprovided in fixed-length formats, formats linking positions of data tocategories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),enabling processes or devices to detect categories of data.

Alternatively or additionally, and still referring to FIG. 1, trainingdata may include one or more elements that are not categorized; that is,training data may not be formatted or contain descriptors for someelements of data. Machine-learning algorithms and/or other processes maysort training data according to one or more categorizations using, forinstance, natural language processing algorithms, tokenization,detection of correlated values in raw data and the like; categories maybe generated using correlation and/or other processing algorithms. As anon-limiting example, in a corpus of text, phrases making up a number“n” of compound words, such as nouns modified by other nouns, may beidentified according to a statistically significant prevalence ofn-grams containing such words in a particular order; such an n-gram maybe categorized as an element of language such as a “word” to be trackedsimilarly to single words, generating a new category as a result ofstatistical analysis. Similarly, in a data entry including some textualdata, a person's name and/or a description of a medical condition ortherapy may be identified by reference to a list, dictionary, or othercompendium of terms, permitting ad-hoc categorization bymachine-learning algorithms, and/or automated association of data in thedata entry with descriptors or into a given format. The ability tocategorize data entries automatedly may enable the same training data tobe made applicable for two or more distinct machine-learning algorithmsas described in further detail below.

Still referring to FIG. 1, diagnostic engine 104 may be configured toreceive a first training set 106 including a plurality of first dataentries, each first data entry of the first training set 106 includingat least an element of physiological state data 108 and at least acorrelated first prognostic label 110. At least an element ofphysiological state data 108 may include any data indicative of aperson's physiological state; physiological state may be evaluated withregard to one or more measures of health of a person's body, one or moresystems within a person's body such as a circulatory system, a digestivesystem, a nervous system, or the like, one or more organs within aperson's body, and/or any other subdivision of a person's body usefulfor diagnostic or prognostic purposes. Physiological state data 108 mayinclude, without limitation, hematological data, such as red blood cellcount, which may include a total number of red blood cells in a person'sblood and/or in a blood sample, hemoglobin levels, hematocritrepresenting a percentage of blood in a person and/or sample that iscomposed of red blood cells, mean corpuscular volume, which may be anestimate of the average red blood cell size, mean corpuscularhemoglobin, which may measure average weight of hemoglobin per red bloodcell, mean corpuscular hemoglobin concentration, which may measure anaverage concentration of hemoglobin in red blood cells, platelet count,mean platelet volume which may measure the average size of platelets,red blood cell distribution width, which measures variation in red bloodcell size, absolute neutrophils, which measures the number of neutrophilwhite blood cells, absolute quantities of lymphocytes such as B-cells,T-cells, Natural Killer Cells, and the like, absolute numbers ofmonocytes including macrophage precursors, absolute numbers ofeosinophils, and/or absolute counts of basophils. Physiological statedata 108 may include, without limitation, immune function data such asInterleukine-6 (IL-6), TNF-alpha, systemic inflammatory cytokines, andthe like.

Continuing to refer to FIG. 1, physiological state data 108 may include,without limitation, data describing blood-born lipids, including totalcholesterol levels, high-density lipoprotein (HDL) cholesterol levels,low-density lipoprotein (LDL) cholesterol levels, very low-densitylipoprotein (VLDL) cholesterol levels, levels of triglycerides, and/orany other quantity of any blood-born lipid or lipid-containingsubstance. Physiological state data 108 may include measures of glucosemetabolism such as fasting glucose levels and/or hemoglobin A1-C (HbA1c)levels. Physiological state data 108 may include, without limitation,one or more measures associated with endocrine function, such as withoutlimitation, quantities of dehydroepiandrosterone (DHEAS), DHEA-Sulfate,quantities of cortisol, ratio of DHEAS to cortisol, quantities oftestosterone quantities of estrogen, quantities of growth hormone (GH),insulin-like growth factor 1 (IGF-1), quantities of adipokines such asadiponectin, leptin, and/or ghrelin, quantities of somatostatin,progesterone, or the like. Physiological state data 108 may includemeasures of estimated glomerular filtration rate (eGFR). Physiologicalstate data 108 may include quantities of C-reactive protein, estradiol,ferritin, folate, homocysteine, prostate-specific Ag,thyroid-stimulating hormone, vitamin D, 25 hydroxy, blood urea nitrogen,creatinine, sodium, potassium, chloride, carbon dioxide, uric acid,albumin, globulin, calcium, phosphorus, alkaline phosphatase, alanineamino transferase, aspartate amino transferase, lactate dehydrogenase(LDH), bilirubin, gamma-glutamyl transferase (GGT), iron, and/or totaliron binding capacity (TIBC), or the like. Physiological state data 108may include antinuclear antibody levels. Physiological state data 108may include aluminum levels. Physiological state data 108 may includearsenic levels. Physiological state data 108 may include levels offibrinogen, plasma cystatin C, and/or brain natriuretic peptide.

Continuing to refer to FIG. 1, physiological state data 108 may includemeasures of lung function such as forced expiratory volume, one second(FEV-1) which measures how much air can be exhaled in one secondfollowing a deep inhalation, forced vital capacity (FVC), which measuresthe volume of air that may be contained in the lungs. Physiologicalstate data 108 may include a measurement blood pressure, includingwithout limitation systolic and diastolic blood pressure. Physiologicalstate data 108 may include a measure of waist circumference.Physiological state data 108 may include body mass index (BMI).Physiological state data 108 may include one or more measures of bonemass and/or density such as dual-energy x-ray absorptiometry.Physiological state data 108 may include one or more measures of musclemass. Physiological state data 108 may include one or more measures ofphysical capability such as without limitation measures of gripstrength, evaluations of standing balance, evaluations of gait speed,pegboard tests, timed up and go tests, and/or chair rising tests.

Still viewing FIG. 1, physiological state data 108 may include one ormore measures of cognitive function, including without limitation Reyauditory verbal learning test results, California verbal learning testresults, NIH toolbox picture sequence memory test, Digital symbol codingevaluations, and/or Verbal fluency evaluations. Physiological state data204 may include one or more measures of psychological function or state,such as without limitation clinical interviews, assessments ofintellectual functioning and/or intelligence quotient (IQ) tests,personality assessments, and/or behavioral assessments. Physiologicalstate data 204 may include one or more psychological self-assessments,which may include any self-administered and/or automatedlycomputer-administered assessments, whether administered within system100 and/or via a third-party service or platform.

Continuing to refer to FIG. 1, physiological state data may includepsychological data. Psychological data may include any data generatedusing psychological, neuro-psychological, and/or cognitive evaluations,as well as diagnostic screening tests, personality tests, personalcompatibility tests, or the like; such data may include, withoutlimitation, numerical score data entered by an evaluating professionaland/or by a subject performing a self-test such as a computerizedquestionnaire. Psychological data may include textual, video, or imagedata describing testing, analysis, and/or conclusions entered by amedical professional such as without limitation a psychologist,psychiatrist, psychotherapist, social worker, a medical doctor, or thelike. Psychological data may include data gathered from userinteractions with persons, documents, and/or computing devices; forinstance, user patterns of purchases, including electronic purchases,communication such as via chat-rooms or the like, any textual, image,video, and/or data produced by the subject, any textual image, videoand/or other data depicting and/or describing the subject, or the like.Any psychological data and/or data used to generate psychological datamay be analyzed using machine-learning and/or language processingmodules as described in this disclosure.

With continued reference to FIG. 1, physiological state data 108 mayinclude one or more evaluations of sensory ability, including measuresof audition, vision, olfaction, gustation, vestibular function and pain.Physiological state data 108 may include genomic data, includingdeoxyribonucleic acid (DNA) samples and/or sequences, such as withoutlimitation DNA sequences contained in one or more chromosomes in humancells. Genomic data may include, without limitation, ribonucleic acid(RNA) samples and/or sequences, such as samples and/or sequences ofmessenger RNA (mRNA) or the like taken from human cells. Genetic datamay include telomere lengths. Genomic data may include epigenetic dataincluding data describing one or more states of methylation of geneticmaterial. Physiological state data 108 may include proteomic data, whichas used herein, is data describing all proteins produced and/or modifiedby an organism, colony of organisms, or system of organisms, and/or asubset thereof. Physiological state data 108 may include data concerninga microbiome of a person, which as used herein, includes any datadescribing any microorganism and/or combination of microorganisms livingon or within a person, including without limitation biomarkers, genomicdata, proteomic data, and/or any other metabolic or biochemical datauseful for analysis of the effect of such microorganisms on otherphysiological state data 108 of a person, and/or on prognostic labelsand/or ameliorative processes as described in further detail below.Physiological state data 108 may include any physiological state data108, as described above, describing any multicellular organism living inor on a person including any parasitic and/or symbiotic organisms livingin or on the persons; non-limiting examples may include mites,nematodes, flatworms, or the like.

With continuing reference to FIG. 1, physiological state data mayinclude one or more user-entered descriptions of a person'sphysiological state. One or more user-entered descriptions may include,without limitation, user descriptions of symptoms, which may includewithout limitation current or past physical, psychological, perceptual,and/or neurological symptoms, user descriptions of current or pastphysical, emotional, and/or psychological problems and/or concerns, userdescriptions of past or current treatments, including therapies,nutritional regimens, exercise regimens, pharmaceuticals or the like, orany other user-entered data that a user may provide to a medicalprofessional when seeking treatment and/or evaluation, and/or inresponse to medical intake papers, questionnaires, questions frommedical professionals, or the like.

With continuing reference to FIG. 1, physiological state data mayinclude one or more user-entered descriptions of a person'sphysiological state. One or more user-entered descriptions may include,without limitation, user descriptions of symptoms, which may includewithout limitation current or past physical, psychological, perceptual,and/or neurological symptoms, user descriptions of current or pastphysical, emotional, and/or psychological problems and/or concerns, userdescriptions of past or current treatments, including therapies,nutritional regimens, exercise regimens, pharmaceuticals or the like, orany other user-entered data that a user may provide to a medicalprofessional when seeking treatment and/or evaluation, and/or inresponse to medical intake papers, questionnaires, questions frommedical professionals, or the like. Physiological state data may includeany physiological state data, as described above, describing anymulticellular organism living in or on a person including any parasiticand/or symbiotic organisms living in or on the persons; non-limitingexamples may include mites, nematodes, flatworms, or the like. Examplesof physiological state data described in this disclosure are presentedfor illustrative purposes only and are not meant to be exhaustive.

With continued reference to FIG. 1, physiological data may include,without limitation any result of any medical test, physiologicalassessment, cognitive assessment, psychological assessment, or the like.System 100 may receive at least a physiological data from one or moreother devices after performance; system 100 may alternatively oradditionally perform one or more assessments and/or tests to obtain atleast a physiological data, and/or one or more portions thereof, onsystem 100. For instance, at least physiological data may include ormore entries by a user in a form or similar graphical user interfaceobject; one or more entries may include, without limitation, userresponses to questions on a psychological, behavioral, personality, orcognitive test. For instance, at least a computing device 102 maypresent to user a set of assessment questions designed or intended toevaluate a current state of mind of the user, a current psychologicalstate of the user, a personality trait of the user, or the like; atleast a computing device 102 may provide user-entered responses to suchquestions directly as at least a physiological data and/or may performone or more calculations or other algorithms to derive a score or otherresult of an assessment as specified by one or more testing protocols,such as automated calculation of a Stanford-Binet and/or Wechsler scalefor IQ testing, a personality test scoring such as a Myers-Briggs testprotocol, or other assessments that may occur to persons skilled in theart upon reviewing the entirety of this disclosure.

With continued reference to FIG. 1, assessment and/or self-assessmentdata, and/or automated or other assessment results, obtained from athird-party device; third-party device may include, without limitation,a server or other device (not shown) that performs automated cognitive,psychological, behavioral, personality, or other assessments.Third-party device may include a device operated by an informed advisor.An informed advisor may include any medical professional who may assistand/or participate in the medical treatment of a user. An informedadvisor may include a medical doctor, nurse, physician assistant,pharmacist, yoga instructor, nutritionist, spiritual healer, meditationteacher, fitness coach, health coach, life coach, and the like.

With continued reference to FIG. 1, physiological data may include datadescribing one or more test results, including results of mobilitytests, stress tests, dexterity tests, endocrinal tests, genetic tests,and/or electromyographic tests, biopsies, radiological tests, genetictests, and/or sensory tests. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various additionalexamples of at least a physiological sample consistent with thisdisclosure.

With continued reference to FIG. 1, physiological data may include oneor more user body measurements. A “user body measurement” as used inthis disclosure, includes a measurable indicator of the severity,absence, and/or presence of a disease state. A “disease state” as usedin this disclosure, includes any harmful deviation from the normalstructural and/or function state of a human being. A disease state mayinclude any medical condition and may be associated with specificsymptoms and signs. A disease state may be classified into differenttypes including infectious diseases, deficiency diseases, hereditarydiseases, and/or physiological diseases. For instance and withoutlimitation, internal dysfunction of the immune system may produce avariety of different diseases including immunodeficiency,hypersensitivity, allergies, and/or auto-immune disorders.

With continued reference to FIG. 1, user body measurements may berelated to particular dimensions of the human body. A “dimension of thehuman body” as used in this disclosure, includes one or more functionalbody systems that are impaired by disease in a human body and/or animalbody. Functional body systems may include one or more body systemsrecognized as attributing to root causes of disease by functionalmedicine practitioners and experts. A “root cause” as used in thisdisclosure, includes any chain of causation describing underlyingreasons for a particular disease state and/or medical condition insteadof focusing solely on symptomatology reversal. Root cause may includechains of causation developed by functional medicine practices that mayfocus on disease causation and reversal. For instance and withoutlimitation, a medical condition such as diabetes may include a chain ofcausation that does not include solely impaired sugar metabolism butthat also includes impaired hormone systems including insulinresistance, high cortisol, less than optimal thyroid production, and lowsex hormones. Diabetes may include further chains of causation thatinclude inflammation, poor diet, delayed food allergies, leaky gut,oxidative stress, damage to cell membranes, and dysbiosis. Dimensions ofthe human body may include but are not limited to epigenetics, gut-wall,microbiome, nutrients, genetics, and/or metabolism.

With continued reference to FIG. 1, epigenetic, as used herein, includesany user body measurements describing changes to a genome that do notinvolve corresponding changes in nucleotide sequence. Epigenetic bodymeasurement may include data describing any heritable phenotypic.Phenotype, as used herein, include any observable trait of a userincluding morphology, physical form, and structure. Phenotype mayinclude a user's biochemical and physiological properties, behavior, andproducts of behavior. Behavioral phenotypes may include cognitive,personality, and behavior patterns. This may include effects on cellularand physiological phenotypic traits that may occur due to external orenvironmental factors. For example, DNA methylation and histonemodification may alter phenotypic expression of genes without alteringunderlying DNA sequence. Epigenetic body measurements may include datadescribing one or more states of methylation of genetic material.

With continued reference to FIG. 1, gut-wall, as used herein, includesthe space surrounding the lumen of the gastrointestinal tract that iscomposed of four layers including the mucosa, submucosa, muscular layer,and serosa. The mucosa contains the gut epithelium that is composed ofgoblet cells that function to secrete mucus, which aids in lubricatingthe passage of food throughout the digestive tract. The goblet cellsalso aid in protecting the intestinal wall from destruction by digestiveenzymes. The mucosa includes villi or folds of the mucosa located in thesmall intestine that increase the surface area of the intestine. Thevilli contain a lacteal, that is a vessel connected to the lymph systemthat aids in removal of lipids and tissue fluids. Villi may containmicrovilli that increase the surface area over which absorption can takeplace. The large intestine lack villi and instead a flat surfacecontaining goblet cells are present.

With continued reference to FIG. 1, gut-wall includes the submucosa,which contains nerves, blood vessels, and elastic fibers containingcollagen. Elastic fibers contained within the submucosa aid instretching the gastrointestinal tract with increased capacity while alsomaintaining the shape of the intestine. Gut-wall includes muscular layerwhich contains smooth muscle that aids in peristalsis and the movementof digested material out of and along the gut. Gut-wall includes theserosa which is composed of connective tissue and coated in mucus toprevent friction damage from the intestine rubbing against other tissue.Mesenteries are also found in the serosa and suspend the intestine inthe abdominal cavity to stop it from being disturbed when a person isphysically active.

With continued reference to FIG. 1, gut-wall body measurement mayinclude data describing one or more test results including results ofgut-wall function, gut-wall integrity, gut-wall strength, gut-wallabsorption, gut-wall permeability, intestinal absorption, gut-wallbarrier function, gut-wall absorption of bacteria, gut-wallmalabsorption, gut-wall gastrointestinal imbalances and the like.

With continued reference to FIG. 1, gut-wall body measurement mayinclude any data describing blood test results of creatinine levels,lactulose levels, zonulin levels, and mannitol levels. Gut-wall bodymeasurement may include blood test results of specific gut-wall bodymeasurements including d-lactate, endotoxin lipopolysaccharide (LPS)Gut-wall body measurement may include data breath tests measuringlactulose, hydrogen, methane, lactose, and the like. Gut-wall bodymeasurement may include blood test results describing blood chemistrylevels of albumin, bilirubin, complete blood count, electrolytes,minerals, sodium, potassium, calcium, glucose, blood clotting factors,

With continued reference to FIG. 1, gut-wall body measurement mayinclude one or more stool test results describing presence or absence ofparasites, firmicutes, Bacteroidetes, absorption, inflammation, foodsensitivities. Stool test results may describe presence, absence, and/ormeasurement of acetate, aerobic bacterial cultures, anerobic bacterialcultures, fecal short chain fatty acids, beta-glucuronidase,cholesterol, chymotrypsin, fecal color, Cryptosporidium EIA, Entamoebahistolytica, fecal lactoferrin, Giardia lamblia EIA, long chain fattyacids, meat fibers and vegetable fibers, mucus, occult blood, parasiteidentification, phospholipids, propionate, putrefactive short chainfatty acids, total fecal fat, triglycerides, yeast culture, n-butyrate,pH and the like.

With continued reference to FIG. 1, gut-wall body measurement mayinclude one or more stool test results describing presence, absence,and/or measurement of microorganisms including bacteria, archaea, fungi,protozoa, algae, viruses, parasites, worms, and the like. Stool testresults may contain species such as Bifidobacterium species,Campylobacter species, Clostridium difficile, Cryptosporidium species,Cyclospora cayetanensis, Cryptosporidium EIA, Dientamoeba fragilis,Entamoeba histolytica, Escherichia coli, Entamoeba histolytica, Giardia,H. pylori, Candida albicans, Lactobacillus species, worms, macroscopicworms, mycology, protozoa, Shiga toxin E. coli, and the like.

With continued reference to FIG. 1, gut-wall body measurement mayinclude one or more microscopic ova exam results, microscopic parasiteexam results, protozoan polymerase chain reaction test results and thelike. Gut-wall body measurement may include enzyme-linked immunosorbentassay (ELISA) test results describing immunoglobulin G (Ig G) foodantibody results, immunoglobulin E (Ig E) food antibody results, Ig Emold results, IgG spice and herb results. Gut-wall body measurement mayinclude measurements of calprotectin, eosinophil protein x (EPX), stoolweight, pancreatic elastase, total urine volume, blood creatininelevels, blood lactulose levels, blood mannitol levels.

With continued reference to FIG. 1, gut-wall body measurement mayinclude one or more elements of data describing one or more proceduresexamining gut including for example colonoscopy, endoscopy, large andsmall molecule challenge and subsequent urinary recovery using largemolecules such as lactulose, polyethylene glycol-3350, and smallmolecules such as mannitol, L-rhamnose, polyethyleneglycol-400. Gut-wallbody measurement may include data describing one or more images such asx-ray, MRI, CT scan, ultrasound, standard barium follow-throughexamination, barium enema, barium with contract, MRI fluoroscopy,positron emission tomography 9PET), diffusion-weighted MRI imaging, andthe like.

With continued reference to FIG. 1, microbiome, as used herein, includesecological community of commensal, symbiotic, and pathogenicmicroorganisms that reside on or within any of a number of human tissuesand biofluids. For example, human tissues and biofluids may include theskin, mammary glands, placenta, seminal fluid, uterus, vagina, ovarianfollicles, lung, saliva, oral mucosa, conjunctiva, biliary, andgastrointestinal tracts. Microbiome may include for example, bacteria,archaea, protists, fungi, and viruses. Microbiome may include commensalorganisms that exist within a human being without causing harm ordisease. Microbiome may include organisms that are not harmful butrather harm the human when they produce toxic metabolites such astrimethylamine. Microbiome may include pathogenic organisms that causehost damage through virulence factors such as producing toxicby-products. Microbiome may include populations of microbes such asbacteria and yeasts that may inhabit the skin and mucosal surfaces invarious parts of the body. Bacteria may include for example Firmicutesspecies, Bacteroidetes species, Proteobacteria species, Verrumicrobiaspecies, Actinobacteria species, Fusobacteria species, Cyanobacteriaspecies and the like. Archaea may include methanogens such asMethanobrevibacter smithies' and Methanosphaera stadtmanae. Fungi mayinclude Candida species and Malassezia species. Viruses may includebacteriophages. Microbiome species may vary in different locationsthroughout the body. For example, the genitourinary system may contain ahigh prevalence of Lactobacillus species while the gastrointestinaltract may contain a high prevalence of Bifidobacterium species while thelung may contain a high prevalence of Streptococcus and Staphylococcusspecies.

With continued reference to FIG. 1, microbiome body measurement mayinclude one or more stool test results describing presence, absence,and/or measurement of microorganisms including bacteria, archaea, fungi,protozoa, algae, viruses, parasites, worms, and the like. Stool testresults may contain species such as Ackerman's muciniphila,Anaerotruncus colihominis, bacteriology, Bacteroides vulgates',Bacteroides-Prevotella, Barnesiella species, Bifidobacterium longarm,Bifidobacterium species, Butyrivbrio crossotus, Clostridium species,Collinsella aerofaciens, fecal color, fecal consistency, Coprococcuseutactus, Desulfovibrio piger, Escherichia coli, Faecalibacteriumprausnitzii, Fecal occult blood, Firmicutes to Bacteroidetes ratio,Fusobacterium species, Lactobacillus species, Methanobrevibactersmithii, yeast minimum inhibitory concentration, bacteria minimuminhibitory concentration, yeast mycology, fungi mycology, Odoribacterspecies, Oxalobacter formigenes, parasitology, Prevotella species,Pseudoflavonifractor species, Roseburia species, Ruminococcus species,Veillonella species and the like.

With continued reference to FIG. 1, microbiome body measurement mayinclude one or more stool tests results that identify all microorganismsliving a user's gut including bacteria, viruses, archaea, yeast, fungi,parasites, and bacteriophages. Microbiome body measurement may includeDNA and RNA sequences from live microorganisms that may impact a user'shealth. Microbiome body measurement may include high resolution of bothspecies and strains of all microorganisms. Microbiome body measurementmay include data describing current microbe activity. Microbiome bodymeasurement may include expression of levels of active microbial genefunctions. Microbiome body measurement may include descriptions ofsources of disease causing microorganisms, such as viruses found in thegastrointestinal tract such as raspberry bushy swarf virus fromconsuming contaminated raspberries or Pepino mosaic virus from consumingcontaminated tomatoes.

With continued reference to FIG. 1, microbiome body measurement mayinclude one or more blood test results that identify metabolitesproduced by microorganisms. Metabolites may include for example,indole-3-propionic acid, indole-3-lactic acid, indole-3-acetic acid,tryptophan, serotonin, kynurenine, total indoxyl sulfate, tyrosine,xanthine, 3-methylxanthine, uric acid, and the like.

With continued reference to FIG. 1, microbiome body measurement mayinclude one or more breath test results that identify certain strains ofmicroorganisms that may be present in certain areas of a user's body.This may include for example, lactose intolerance breath tests,methane-based breath tests, hydrogen based breath tests, fructose basedbreath tests. Helicobacter pylori breath test, fructose intolerancebreath test, bacterial overgrowth syndrome breath tests and the like.

With continued reference to FIG. 1, microbiome body measurement mayinclude one or more urinary analysis results for certain microbialstrains present in urine. This may include for example, urinalysis thatexamines urine specific gravity, urine cytology, urine sodium, urineculture, urinary calcium, urinary hematuria, urinary glucose levels,urinary acidity, urinary protein, urinary nitrites, bilirubin, red bloodcell urinalysis, and the like.

With continued reference to FIG. 1, nutrient as used herein, includesany substance required by the human body to function. Nutrients mayinclude carbohydrates, protein, lipids, vitamins, minerals,antioxidants, fatty acids, amino acids, and the like. Nutrients mayinclude for example vitamins such as thiamine, riboflavin, niacin,pantothenic acid, pyridoxine, biotin, folate, cobalamin, Vitamin C,Vitamin A, Vitamin D, Vitamin E, and Vitamin K. Nutrients may includefor example minerals such as sodium, chloride, potassium, calcium,phosphorous, magnesium, sulfur, iron, zinc, iodine, selenium, copper,manganese, fluoride, chromium, molybdenum, nickel, aluminum, silicon,vanadium, arsenic, and boron.

With continued reference to FIG. 1, nutrients may include extracellularnutrients that are free floating in blood and exist outside of cells.Extracellular nutrients may be located in serum. Nutrients may includeintracellular nutrients which may be absorbed by cells including whiteblood cells and red blood cells.

With continued reference to FIG. 1, nutrient body measurement mayinclude one or more blood test results that identify extracellular andintracellular levels of nutrients. Nutrient body measurement may includeblood test results that identify serum, white blood cell, and red bloodcell levels of nutrients. For example, nutrient body measurement mayinclude serum, white blood cell, and red blood cell levels ofmicronutrients such as Vitamin A, Vitamin B1, Vitamin B2, Vitamin B3,Vitamin B6, Vitamin B12, Vitamin B5, Vitamin C, Vitamin D, Vitamin E,Vitamin K1, Vitamin K2, and folate.

With continued reference to FIG. 1, nutrient body measurement mayinclude one or more blood test results that identify serum, white bloodcell and red blood cell levels of nutrients such as calcium, manganese,zinc, copper, chromium, iron, magnesium, copper to zinc ratio, choline,inositol, carnitine, methylmalonic acid (MMA), sodium, potassium,asparagine, glutamine, serine, coenzyme q10, cysteine, alpha lipoicacid, glutathione, selenium, eicosapentaenoic acid (EPA),docosahexaenoic acid (DHA), docosapentaenoic acid (DPA), total omega-3,lauric acid, arachidonic acid, oleic acid, total omega 6, and omega 3index.

With continued reference to FIG. 1, nutrient body measurement mayinclude one or more salivary test results that identify levels ofnutrients including any of the nutrients as described herein. Nutrientbody measurement may include hair analysis of levels of nutrientsincluding any of the nutrients as described herein.

With continued reference to FIG. 1, genetic as used herein, includes anyinherited trait. Inherited traits may include genetic material containedwith DNA including for example, nucleotides. Nucleotides include adenine(A), cytosine (C), guanine (G), and thymine (T). Genetic information maybe contained within the specific sequence of an individual's nucleotidesand sequence throughout a gene or DNA chain. Genetics may include how aparticular genetic sequence may contribute to a tendency to develop acertain disease such as cancer or Alzheimer's disease.

With continued reference to FIG. 1, genetic body measurement may includeone or more results from one or more blood tests, hair tests, skintests, urine, amniotic fluid, buccal swabs and/or tissue test toidentify a user's particular sequence of nucleotides, genes,chromosomes, and/or proteins. Genetic body measurement may include teststhat example genetic changes that may lead to genetic disorders. Geneticbody measurement may detect genetic changes such as deletion of geneticmaterial or pieces of chromosomes that may cause Duchenne MuscularDystrophy. Genetic body measurement may detect genetic changes such asinsertion of genetic material into DNA or a gene such as the BRCA1 genethat is associated with an increased risk of breast and ovarian cancerdue to insertion of 2 extra nucleotides. Genetic body measurement mayinclude a genetic change such as a genetic substitution from a piece ofgenetic material that replaces another as seen with sickle cell anemiawhere one nucleotide is substituted for another. Genetic bodymeasurement may detect a genetic change such as a duplication when extragenetic material is duplicated one or more times within a person'sgenome such as with Charcot-Marie Tooth disease type 1. Genetic bodymeasurement may include a genetic change such as an amplification whenthere is more than a normal number of copies of a gene in a cell such asHER2 amplification in cancer cells. Genetic body measurement may includea genetic change such as a chromosomal translocation when pieces ofchromosomes break off and reattach to another chromosome such as withthe BCR-ABL1 gene sequence that is formed when pieces of chromosome 9and chromosome 22 break off and switch places. Genetic body measurementmay include a genetic change such as an inversion when one chromosomeexperiences two breaks and the middle piece is flipped or invertedbefore reattaching. Genetic body measurement may include a repeat suchas when regions of DNA contain a sequence of nucleotides that repeat anumber of times such as for example in Huntington's disease or Fragile Xsyndrome. Genetic body measurement may include a genetic change such asa trisomy when there are three chromosomes instead of the usual pair asseen with Down syndrome with a trisomy of chromosome 21, Edwardssyndrome with a trisomy at chromosome 18 or Patau syndrome with atrisomy at chromosome 13. Genetic body measurement may include a geneticchange such as monosomy such as when there is an absence of a chromosomeinstead of a pair, such as in Turner syndrome.

With continued reference to FIG. 1, genetic body measurement may includean analysis of COMT gene that is responsible for producing enzymes thatmetabolize neurotransmitters. Genetic body measurement may include ananalysis of DRD2 gene that produces dopamine receptors in the brain.Genetic body measurement may include an analysis of ADRA2B gene thatproduces receptors for noradrenaline. Genetic body measurement mayinclude an analysis of 5-HTTLPR gene that produces receptors forserotonin. Genetic body measurement may include an analysis of BDNF genethat produces brain derived neurotrophic factor. Genetic bodymeasurement may include an analysis of 9p21 gene that is associated withcardiovascular disease risk. Genetic body measurement may include ananalysis of APOE gene that is involved in the transportation of bloodlipids such as cholesterol. Genetic body measurement may include ananalysis of NOS3 gene that is involved in producing enzymes involved inregulating vaso-dilation and vaso-constriction of blood vessels.

With continued reference to FIG. 1, genetic body measurement may includeACE gene that is involved in producing enzymes that regulate bloodpressure. Genetic body measurement may include SLCO1B1 gene that directspharmaceutical compounds such as statins into cells. Genetic bodymeasurement may include FUT2 gene that produces enzymes that aid inabsorption of Vitamin B12 from digestive tract. Genetic body measurementmay include MTHFR gene that is responsible for producing enzymes thataid in metabolism and utilization of Vitamin B9 or folate. Genetic bodymeasurement may include SHMT1 gene that aids in production andutilization of Vitamin B9 or folate. Genetic body measurement mayinclude MTRR gene that produces enzymes that aid in metabolism andutilization of Vitamin B12. Genetic body measurement may include MTRgene that produces enzymes that aid in metabolism and utilization ofVitamin B12. Genetic body measurement may include FTO gene that aids infeelings of satiety or fullness after eating. Genetic body measurementmay include MC4R gene that aids in producing hunger cues and hungertriggers. Genetic body measurement may include APOA2 gene that directsbody to produce ApoA2 thereby affecting absorption of saturated fats.Genetic body measurement may include UCP1 gene that aids in controllingmetabolic rate and thermoregulation of body. Genetic body measurementmay include TCF7L2 gene that regulates insulin secretion. Genetic bodymeasurement may include AMY1 gene that aids in digestion of starchyfoods. Genetic body measurement may include MCM6 gene that controlsproduction of lactase enzyme that aids in digesting lactose found indairy products. Genetic body measurement may include BCMO1 gene thataids in producing enzymes that aid in metabolism and activation ofVitamin A. Genetic body measurement may include SLC23A1 gene thatproduce and transport Vitamin C. Genetic body measurement may includeCYP2R1 gene that produce enzymes involved in production and activationof Vitamin D. Genetic body measurement may include GC gene that produceand transport Vitamin D. Genetic body measurement may include CYP1A2gene that aid in metabolism and elimination of caffeine. Genetic bodymeasurement may include CYP17A1 gene that produce enzymes that convertprogesterone into androgens such as androstenedione, androstendiol,dehydroepiandrosterone, and testosterone.

With continued reference to FIG. 1, genetic body measurement may includeCYP19A1 gene that produce enzymes that convert androgens such asandrostenedione and testosterone into estrogens including estradiol andestrone. Genetic body measurement may include SRD5A2 gene that aids inproduction of enzymes that convert testosterone intodihydrotestosterone. Genetic body measurement may include UFT2B17 genethat produces enzymes that metabolize testosterone anddihydrotestosterone. Genetic body measurement may include CYP1A1 genethat produces enzymes that metabolize estrogens into 2 hydroxy-estrogen.Genetic body measurement may include CYP1B1 gene that produces enzymesthat metabolize estrogens into 4 hydroxy-estrogen. Genetic bodymeasurement may include CYP3A4 gene that produces enzymes thatmetabolize estrogen into 16 hydroxy-estrogen. Genetic body measurementmay include COMT gene that produces enzymes that metabolize 2hydroxy-estrogen and 4 hydroxy-estrogen into methoxy estrogen. Geneticbody measurement may include GSTT1 gene that produces enzymes thateliminate toxic by-products generated from metabolism of estrogens.Genetic body measurement may include GSTM1 gene that produces enzymesresponsible for eliminating harmful by-products generated frommetabolism of estrogens. Genetic body measurement may include GSTP1 genethat produces enzymes that eliminate harmful by-products generated frommetabolism of estrogens. Genetic body measurement may include SOD2 genethat produces enzymes that eliminate oxidant by-products generated frommetabolism of estrogens.

With continued reference to FIG. 1, metabolic, as used herein, includesany process that converts food and nutrition into energy. Metabolic mayinclude biochemical processes that occur within the body. Metabolic bodymeasurement may include blood tests, hair tests, skin tests, amnioticfluid, buccal swabs and/or tissue test to identify a user's metabolism.Metabolic body measurement may include blood tests that examine glucoselevels, electrolytes, fluid balance, kidney function, and liverfunction. Metabolic body measurement may include blood tests thatexamine calcium levels, albumin, total protein, chloride levels, sodiumlevels, potassium levels, carbon dioxide levels, bicarbonate levels,blood urea nitrogen, creatinine, alkaline phosphatase, alanine aminotransferase, aspartate amino transferase, bilirubin, and the like.

With continued reference to FIG. 1, metabolic body measurement mayinclude one or more blood, saliva, hair, urine, skin, and/or buccalswabs that examine levels of hormones within the body such as11-hydroxy-androstereone, 11-hydroxy-etiocholanolone,11-keto-androsterone, 11-keto-etiocholanolone, 16 alpha-hydroxyestrone,2-hydroxyestrone, 4-hydroxyestrone, 4-methoxyestrone, androstanediol,androsterone, creatinine, DHEA, estradiol, estriol, estrone,etiocholanolone, pregnanediol, pregnanestriol, specific gravity,testosterone, tetrahydrocortisol, tetrahydrocrotisone,tetrahydrodeoxycortisol, allo-tetrahydrocortisol.

With continued reference to FIG. 1, metabolic body measurement mayinclude one or more metabolic rate test results such as breath teststhat may analyze a user's resting metabolic rate or number of caloriesthat a user's body burns each day rest. Metabolic body measurement mayinclude one or more vital signs including blood pressure, breathingrate, pulse rate, temperature, and the like. Metabolic body measurementmay include blood tests such as a lipid panel such as low densitylipoprotein (LDL), high density lipoprotein (HDL), triglycerides, totalcholesterol, ratios of lipid levels such as total cholesterol to HDLratio, insulin sensitivity test, fasting glucose test, Hemoglobin A1Ctest, adipokines such as leptin and adiponectin, neuropeptides such asghrelin, pro-inflammatory cytokines such as interleukin 6 or tumornecrosis factor alpha, anti-inflammatory cytokines such as interleukin10, markers of antioxidant status such as oxidized low-densitylipoprotein, uric acid, paraoxonase 1. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variousadditional examples of physiological state data that may be usedconsistently with descriptions of systems and methods as provided inthis disclosure.

With continued reference to FIG. 1, physiological data may be obtainedfrom a physically extracted sample. A “physical sample” as used in thisexample, may include any sample obtained from a human body of a user. Aphysical sample may be obtained from a bodily fluid and/or tissueanalysis such as a blood sample, tissue, sample, buccal swab, mucoussample, stool sample, hair sample, fingernail sample and the like. Aphysical sample may be obtained from a device in contact with a humanbody of a user such as a microchip embedded in a user's skin, a sensorin contact with a user's skin, a sensor located on a user's tooth, andthe like. Physiological data may be obtained from a physically extractedsample. A physical sample may include a signal from a sensor configuredto detect physiological data of a user and record physiological data asa function of the signal. A sensor may include any medical sensor and/ormedical device configured to capture sensor data concerning a patient,including any scanning, radiological and/or imaging device such aswithout limitation x-ray equipment, computer assisted tomography (CAT)scan equipment, positron emission tomography (PET) scan equipment, anyform of magnetic resonance imagery (MRI) equipment, ultrasoundequipment, optical scanning equipment such as photo-plethysmographicequipment, or the like. A sensor may include any electromagnetic sensor,including without limitation electroencephalographic sensors,magnetoencephalographic sensors, electrocardiographic sensors,electromyographic sensors, or the like. A sensor may include atemperature sensor. A sensor may include any sensor that may be includedin a mobile device and/or wearable device, including without limitationa motion sensor such as an inertial measurement unit (IMU), one or moreaccelerometers, one or more gyroscopes, one or more magnetometers, orthe like. At least a wearable and/or mobile device sensor may capturestep, gait, and/or other mobility data, as well as data describingactivity levels and/or physical fitness. At least a wearable and/ormobile device sensor may detect heart rate or the like. A sensor maydetect any hematological parameter including blood oxygen level, pulserate, heart rate, pulse rhythm, blood sugar, and/or blood pressure. Asensor may be configured to detect internal and/or external biomarkersand/or readings. A sensor may be a part of system 100 or may be aseparate device in communication with system 100.

With continued reference to FIG. 1, examples of physiological state data108 described in this disclosure are presented for illustrative purposesonly and are not meant to be exhaustive. Persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variousadditional examples of physiological state data 108 that may be usedconsistently with descriptions of systems and methods as provided inthis disclosure.

Continuing to refer to FIG. 1, each element of first training set 106includes at least a first prognostic label 110. A prognostic label, asdescribed herein, is an element of data identifying and/or describing acurrent, incipient, or probable future medical condition affecting aperson; medical condition may include a particular disease, one or moresymptoms associated with a syndrome, a syndrome, and/or any othermeasure of current or future health and/or healthy aging. At least aprognostic label may be associated with a physical and/or somaticcondition, a mental condition such as a mental illness, neurosis, or thelike, or any other condition affecting human health that may beassociated with one or more elements of physiological state data 108 asdescribed in further detail below. Conditions associated with prognosticlabels may include, without limitation one or more diseases, defined forpurposes herein as conditions that negatively affect structure and/orfunction of part or all of an organism. Conditions associated withprognostic labels may include, without limitation, acute or chronicinfections, including without limitation infections by bacteria,archaea, viruses, viroids, prions, single-celled eukaryotic organismssuch as amoeba, paramecia, trypanosomes, plasmodia, Leishmania, and/orfungi, and/or multicellular parasites such as nematodes, arthropods,fungi, or the like. Prognostic labels may be associated with one or moreimmune disorders, including without limitation immunodeficiencies and/orauto-immune conditions. Prognostic labels may be associated with one ormore metabolic disorders. Prognostic labels may be associated with oneor more endocrinal disorders. Prognostic labels may be associated withone or more cardiovascular disorders. Prognostic labels may beassociated with one or more respiratory disorders. Prognostic labels maybe associated with one or more disorders affecting connective tissue.Prognostic labels may be associated with one or more digestivedisorders. Prognostic labels may be associated with one or moreneurological disorders such as neuromuscular disorders, dementia, or thelike. Prognostic labels may be associated with one or more disorders ofthe excretory system, including without limitation nephrologicaldisorders. Prognostic labels may be associated with one or more liverdisorders. Prognostic labels may be associated with one or moredisorders of the bones such as osteoporosis. Prognostic labels may beassociated with one or more disorders affecting joints, such asosteoarthritis, gout, and/or rheumatoid arthritis. Prognostic labels beassociated with one or more cancers, including without limitationcarcinomas, lymphomas, leukemias, germ cell tumor cancers, blastomas,and/or sarcomas. Prognostic labels may include descriptors of latent,dormant, and/or apparent disorders, diseases, and/or conditions.Prognostic labels may include descriptors of conditions for which aperson may have a higher than average probability of development, suchas a condition for which a person may have a “risk factor”; forinstance, a person currently suffering from abdominal obesity may have ahigher than average probability of developing type II diabetes. Theabove-described examples are presented for illustrative purposes onlyand are not intended to be exhaustive. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variousadditional examples of conditions that may be associated with prognosticlabels as described in this disclosure.

Still referring to FIG. 1, at least a prognostic label may be stored inany suitable data and/or data type. For instance, and withoutlimitation, at least a prognostic label may include textual data, suchas numerical, character, and/or string data. Textual data may include astandardized name and/or code for a disease, disorder, or the like;codes may include diagnostic codes and/or diagnosis codes, which mayinclude without limitation codes used in diagnosis classificationsystems such as The International Statistical Classification of Diseasesand Related Health Problems (ICD). In general, there is no limitation onforms textual data or non-textual data used as at least a prognosticlabel may take; persons skilled in the art, upon reviewing the entiretyof this disclosure, will be aware of various forms which may be suitablefor use as at least a prognostic label consistently with thisdisclosure.

With continued reference to FIG. 1, in each first data element of firsttraining set 106, at least a first prognostic label 110 of the dataelement is correlated with at least an element of physiological statedata 108 of the data element. In an embodiment, an element ofphysiological data is correlated with a prognostic label where theelement of physiological data is located in the same data element and/orportion of data element as the prognostic label; for example, andwithout limitation, an element of physiological data is correlated witha prognostic element where both element of physiological data andprognostic element are contained within the same first data element ofthe first training set 106. As a further example, an element ofphysiological data is correlated with a prognostic element where bothshare a category label as described in further detail below, where eachis within a certain distance of the other within an ordered collectionof data in data element, or the like. Still further, an element ofphysiological data may be correlated with a prognostic label where theelement of physiological data and the prognostic label share an origin,such as being data that was collected with regard to a single person orthe like. In an embodiment, a first datum may be more closely correlatedwith a second datum in the same data element than with a third datumcontained in the same data element; for instance, the first element andthe second element may be closer to each other in an ordered set of datathan either is to the third element, the first element and secondelement may be contained in the same subdivision and/or section of datawhile the third element is in a different subdivision and/or section ofdata, or the like. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various forms and/ordegrees of correlation between physiological data and prognostic labelsthat may exist in first training set 106 and/or first data elementconsistently with this disclosure.

In an embodiment, and still referring to FIG. 1, diagnostic engine 104may be designed and configured to associate at least an element ofphysiological state data 108 with at least a category from a list ofsignificant categories of physiological state data 108. Significantcategories of physiological state data 108 may include labels and/ordescriptors describing types of physiological state data 108 that areidentified as being of high relevance in identifying prognostic labels.As a non-limiting example, one or more categories may identifysignificant categories of physiological state data 108 based on degreeof diagnostic relevance to one or more impactful conditions and/orwithin one or more medical or public health fields. For instance, andwithout limitation, a particular set of biomarkers, test results, and/orbiochemical information may be recognized in a given medical field asuseful for identifying various disease conditions or prognoses within arelevant field. As a non-limiting example, and without limitation,physiological data describing red blood cells, such as red blood cellcount, hemoglobin levels, hematocrit, mean corpuscular volume, meancorpuscular hemoglobin, and/or mean corpuscular hemoglobin concentrationmay be recognized as useful for identifying various conditions such asdehydration, high testosterone, nutrient deficiencies, kidneydysfunction, chronic inflammation, anemia, and/or blood loss. As anadditional example, hemoglobin levels may be useful for identifyingelevated testosterone, poor oxygen deliverability, thiamin deficiency,insulin resistance, anemia, liver disease, hypothyroidism, argininedeficiency, protein deficiency, inflammation, and/or nutrientdeficiencies. In a further non-limiting example, hematocrit may beuseful for identifying dehydration, elevated testosterone, poor oxygendeliverability, thiamin deficiency, insulin resistance, anemia, liverdisease, hypothyroidism, arginine deficiency, protein deficiency,inflammation, and/or nutrient deficiencies. Similarly, measures of lipidlevels in blood, such as total cholesterol, HDL, LDL, VLDL,triglycerides, LDL-C and/or HDL-C may be recognized as useful inidentifying conditions such as poor thyroid function, insulinresistance, blood glucose dysregulation, magnesium deficiency,dehydration, kidney disease, familial hypercholesterolemia, liverdysfunction, oxidative stress, inflammation, malabsorption, anemia,alcohol abuse, diabetes, hypercholesterolemia, coronary artery disease,atherosclerosis, or the like. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various additionalcategories of physiological data that may be used consistently with thisdisclosure.

Still referring to FIG. 1, diagnostic engine 104 may receive the list ofsignificant categories according to any suitable process; for instance,and without limitation, diagnostic engine 104 may receive the list ofsignificant categories from at least an expert. In an embodiment,diagnostic engine 104 and/or a user device connected to diagnosticengine 104 may provide a graphical user interface, which may includewithout limitation a form or other graphical element having data entryfields, wherein one or more experts, including without limitationclinical and/or scientific experts, may enter information describing oneor more categories of physiological data that the experts consider to besignificant or useful for detection of conditions; fields in graphicaluser interface may provide options describing previously identifiedcategories, which may include a comprehensive or near-comprehensive listof types of physiological data detectable using known or recordedtesting methods, for instance in “drop-down” lists, where experts may beable to select one or more entries to indicate their usefulness and/orsignificance in the opinion of the experts. Fields may include free-formentry fields such as text-entry fields where an expert may be able totype or otherwise enter text, enabling expert to propose or suggestcategories not currently recorded. Graphical user interface or the likemay include fields corresponding to prognostic labels, where experts mayenter data describing prognostic labels and/or categories of prognosticlabels the experts consider related to entered categories ofphysiological data; for instance, such fields may include drop-downlists or other pre-populated data entry fields listing currentlyrecorded prognostic labels, and which may be comprehensive, permittingeach expert to select a prognostic label and/or a plurality ofprognostic labels the expert believes to be predicted and/or associatedwith each category of physiological data selected by the expert. Fieldsfor entry of prognostic labels and/or categories of prognostic labelsmay include free-form data entry fields such as text entry fields; asdescribed above, examiners may enter data not presented in pre-populateddata fields in the free-form data entry fields. Alternatively oradditionally, fields for entry of prognostic labels may enable an expertto select and/or enter information describing or linked to a category ofprognostic label that the expert considers significant, wheresignificance may indicate likely impact on longevity, mortality, qualityof life, or the like as described in further detail below. Graphicaluser interface may provide an expert with a field in which to indicate areference to a document describing significant categories ofphysiological data, relationships of such categories to prognosticlabels, and/or significant categories of prognostic labels. Any datadescribed above may alternatively or additionally be received fromexperts similarly organized in paper form, which may be captured andentered into data in a similar way, or in a textual form such as aportable document file (PDF) with examiner entries, or the like

Still referring to FIG. 1, diagnostic engine 104 may receive the list ofsignificant categories according to any suitable process; for instance,and without limitation, diagnostic engine 104 may receive the list ofsignificant categories from at least an expert. In an embodiment,diagnostic engine 104 and/or a user device connected to diagnosticengine 104 may provide a graphical user interface 112, which may includewithout limitation a form or other graphical element having data entryfields, wherein one or more experts, including without limitationclinical and/or scientific experts, may enter information describing oneor more categories of physiological data that the experts consider to besignificant or useful for detection of conditions; fields in graphicaluser interface may provide options describing previously identifiedcategories, which may include a comprehensive or near-comprehensive listof types of physiological data detectable using known or recordedtesting methods, for instance in “drop-down” lists, where experts may beable to select one or more entries to indicate their usefulness and/orsignificance in the opinion of the experts. Fields may include free-formentry fields such as text-entry fields where an expert may be able totype or otherwise enter text, enabling expert to propose or suggestcategories not currently recorded. Graphical user interface 112 or thelike may include fields corresponding to prognostic labels, whereexperts may enter data describing prognostic labels and/or categories ofprognostic labels the experts consider related to entered categories ofphysiological data; for instance, such fields may include drop-downlists or other pre-populated data entry fields listing currentlyrecorded prognostic labels, and which may be comprehensive, permittingeach expert to select a prognostic label and/or a plurality ofprognostic labels the expert believes to be predicted and/or associatedwith each category of physiological data selected by the expert. Fieldsfor entry of prognostic labels and/or categories of prognostic labelsmay include free-form data entry fields such as text entry fields; asdescribed above, examiners may enter data not presented in pre-populateddata fields in the free-form data entry fields. Alternatively oradditionally, fields for entry of prognostic labels may enable an expertto select and/or enter information describing or linked to a category ofprognostic label that the expert considers significant, wheresignificance may indicate likely impact on longevity, mortality, qualityof life, or the like as described in further detail below. Graphicaluser interface 112 may provide an expert with a field in which toindicate a reference to a document describing significant categories ofphysiological data, relationships of such categories to prognosticlabels, and/or significant categories of prognostic labels. Any datadescribed above may alternatively or additionally be received fromexperts similarly organized in paper form, which may be captured andentered into data in a similar way, or in a textual form such as aportable document file (PDF) with examiner entries, or the like

With continued reference to FIG. 1, data information describingsignificant categories of physiological data, relationships of suchcategories to prognostic labels, and/or significant categories ofprognostic labels may alternatively or additionally be extracted fromone or more documents using a language processing module 114. Languageprocessing module 114 may include any hardware and/or software module.Language processing module 114 may be configured to extract, from theone or more documents, one or more words. One or more words may include,without limitation, strings of one or characters, including withoutlimitation any sequence or sequences of letters, numbers, punctuation,diacritic marks, engineering symbols, geometric dimensioning; andtolerancing (GD&T) symbols, chemical symbols and formulas, spaces,whitespace, and other symbols, including any symbols usable as textualdata as described above. Textual data may be parsed into tokens, whichmay include a simple word (sequence of letters separated by whitespace)or more generally a sequence of characters as described previously. Theterm “token,” as used herein, refers to any smaller, individualgroupings of text from a larger source of text; tokens may be broken upby word, pair of words, sentence, or other delimitation. These tokensmay in turn be parsed in various ways. Textual data may be parsed intowords or sequences of words, which may be considered words as well.Textual data may be parsed into “n-grams”, where all sequences of nconsecutive characters are considered. Any or all possible sequences oftokens or words may be stored as “chains” for example for use as aMarkov chain or Hidden Markov Model.

Still referring to FIG. 1, language processing module 114 may compareextracted words to categories of physiological data recorded atdiagnostic engine 104, one or more prognostic labels recorded atdiagnostic engine 104, and/or one or more categories of prognosticlabels recorded at diagnostic engine 104; such data for comparison maybe entered on diagnostic engine 104 as described above using expert datainputs or the like. In an embodiment, one or more categories may beenumerated, to find total count of mentions in such documents.Alternatively or additionally, language processing module 114 mayoperate to produce a language processing model. Language processingmodel may include a program automatically generated by diagnostic engine104 and/or language processing module 114 to produce associationsbetween one or more words extracted from at least a document and detectassociations, including without limitation mathematical associations,between such words, and/or associations of extracted words withcategories of physiological data, relationships of such categories toprognostic labels, and/or categories of prognostic labels. Associationsbetween language elements, where language elements include for purposesherein extracted words, categories of physiological data, relationshipsof such categories to prognostic labels, and/or categories of prognosticlabels may include, without limitation, mathematical associations,including without limitation statistical correlations between anylanguage element and any other language element and/or languageelements. Statistical correlations and/or mathematical associations mayinclude probabilistic formulas or relationships indicating, forinstance, a likelihood that a given extracted word indicates a givencategory of physiological data, a given relationship of such categoriesto prognostic labels, and/or a given category of prognostic labels. As afurther example, statistical correlations and/or mathematicalassociations may include probabilistic formulas or relationshipsindicating a positive and/or negative association between at least anextracted word and/or a given category of physiological data, a givenrelationship of such categories to prognostic labels, and/or a givencategory of prognostic labels; positive or negative indication mayinclude an indication that a given document is or is not indicating acategory of physiological data, relationship of such category toprognostic labels, and/or category of prognostic labels is or is notsignificant. For instance, and without limitation, a negative indicationmay be determined from a phrase such as “telomere length was not foundto be an accurate predictor of overall longevity,” whereas a positiveindication may be determined from a phrase such as “telomere length wasfound to be an accurate predictor of dementia,” as an illustrativeexample; whether a phrase, sentence, word, or other textual element in adocument or corpus of documents constitutes a positive or negativeindicator may be determined, in an embodiment, by mathematicalassociations between detected words, comparisons to phrases and/or wordsindicating positive and/or negative indicators that are stored in memoryat diagnostic engine 104, or the like.

Still referring to FIG. 1, language processing module 114 and/ordiagnostic engine 104 may generate the language processing model by anysuitable method, including without limitation a natural languageprocessing classification algorithm; language processing model mayinclude a natural language process classification model that enumeratesand/or derives statistical relationships between input term and outputterms. Algorithm to generate language processing model may include astochastic gradient descent algorithm, which may include a method thatiteratively optimizes an objective function, such as an objectivefunction representing a statistical estimation of relationships betweenterms, including relationships between input terms and output terms, inthe form of a sum of relationships to be estimated. in an alternative oradditional approach, sequential tokens may be modeled as chains, servingas the observations in a Hidden Markov Model (HMM). HMMs as used herein,are statistical models with inference algorithms that that may beapplied to the models. In such models, a hidden state to be estimatedmay include an association between an extracted word category ofphysiological data, a given relationship of such categories toprognostic labels, and/or a given category of prognostic labels. Theremay be a finite number of category of physiological data, a givenrelationship of such categories to prognostic labels, and/or a givencategory of prognostic labels to which an extracted word may pertain; anHMM inference algorithm, such as the forward-backward algorithm or theViterbi algorithm, may be used to estimate the most likely discretestate given a word or sequence of words. Language processing module 114may combine two or more approaches. For instance, and withoutlimitation, machine-learning program may use a combination ofNaive-Bayes (NB), Stochastic Gradient Descent (SGD), and parametergrid-searching classification techniques; the result may include aclassification algorithm that returns ranked associations.

Continuing to refer to FIG. 1, generating language processing model mayinclude generating a vector space, which may be a collection of vectors,defined as a set of mathematical objects that can be added togetherunder an operation of addition following properties of associativity,commutativity, existence of an identity element, and existence of aninverse element for each vector, and can be multiplied by scalar valuesunder an operation of scalar multiplication compatible with fieldmultiplication, and that has an identity element is distributive withrespect to vector addition, and is distributive with respect to fieldaddition. Each vector in an n-dimensional vector space may berepresented by an n-tuple of numerical values. Each unique extractedword and/or language element as described above may be represented by avector of the vector space. In an embodiment, each unique extractedand/or other language element may be represented by a dimension ofvector space; as a non-limiting example, each element of a vector mayinclude a number representing an enumeration of co-occurrences of theword and/or language element represented by the vector with another wordand/or language element. Vectors may be normalized, scaled according torelative frequencies of appearance and/or file sizes. In an embodimentassociating language elements to one another as described above mayinclude computing a degree of vector similarity between a vectorrepresenting each language element and a vector representing anotherlanguage element; vector similarity may be measured according to anynorm for proximity and/or similarity of two vectors, including withoutlimitation cosine similarity, which measures the similarity of twovectors by evaluating the cosine of the angle between the vectors, whichcan be computed using a dot product of the two vectors divided by thelengths of the two vectors. Degree of similarity may include any othergeometric measure of distance between vectors.

Still referring to FIG. 1, language processing module 114 may use acorpus of documents to generate associations between language elementsin a language processing module 114, and diagnostic engine 104 may thenuse such associations to analyze words extracted from one or moredocuments and determine that the one or more documents indicatesignificance of a category of physiological data, a given relationshipof such categories to prognostic labels, and/or a given category ofprognostic labels. In an embodiment, diagnostic engine 104 may performthis analysis using a selected set of significant documents, such asdocuments identified by one or more experts as representing goodscience, good clinical analysis, or the like; experts may identify orenter such documents via graphical user interface as described above inreference to FIG. 9, or may communicate identities of significantdocuments according to any other suitable method of electroniccommunication, or by providing such identity to other persons who mayenter such identifications into diagnostic engine 104. Documents may beentered into diagnostic engine 104 by being uploaded by an expert orother persons using, without limitation, file transfer protocol (FTP) orother suitable methods for transmission and/or upload of documents;alternatively or additionally, where a document is identified by acitation, a uniform resource identifier (URI), uniform resource locator(URL) or other datum permitting unambiguous identification of thedocument, diagnostic engine 104 may automatically obtain the documentusing such an identifier, for instance by submitting a request to adatabase or compendium of documents such as JSTOR as provided by IthakaHarbors, Inc. of New York.

Continuing to refer to FIG. 1, whether an entry indicating significanceof a category of physiological data, a given relationship of suchcategories to prognostic labels, and/or a given category of prognosticlabels is entered via graphical user interface, alternative submissionmeans, and/or extracted from a document or body of documents asdescribed above, an entry or entries may be aggregated to indicate anoverall degree of significance. For instance, each category ofphysiological data, relationship of such categories to prognosticlabels, and/or category of prognostic labels may be given an overallsignificance score; overall significance score may, for instance, beincremented each time an expert submission and/or paper indicatessignificance as described above. Persons skilled in the art, uponreviewing the entirety of this disclosure will be aware of other ways inwhich scores may be generated using a plurality of entries, includingaveraging, weighted averaging, normalization, and the like. Significancescores may be ranked; that is, all categories of physiological data,relationships of such categories to prognostic labels, and/or categoriesof prognostic labels may be ranked according significance scores, forinstance by ranking categories of physiological data, relationships ofsuch categories to prognostic labels, and/or categories of prognosticlabels higher according to higher significance scores and loweraccording to lower significance scores. Categories of physiologicaldata, relationships of such categories to prognostic labels, and/orcategories of prognostic labels may be eliminated from current use ifthey fail a threshold comparison, which may include a comparison ofsignificance score to a threshold number, a requirement thatsignificance score belong to a given portion of ranking such as athreshold percentile, quartile, or number of top-ranked scores.Significance scores may be used to filter outputs as described infurther detail below; for instance, where a number of outputs aregenerated and automated selection of a smaller number of outputs isdesired, outputs corresponding to higher significance scores may beidentified as more probable and/or selected for presentation while otheroutputs corresponding to lower significance scores may be eliminated.Alternatively or additionally, significance scores may be calculated persample type; for instance, entries by experts, documents, and/ordescriptions of purposes of a given type of physiological test or samplecollection as described above may indicate that for that type ofphysiological test or sample collection a first category ofphysiological data, relationship of such category to prognostic labels,and/or category of prognostic labels is significant with regard to thattest, while a second category of physiological data, relationship ofsuch category to prognostic labels, and/or category of prognostic labelsis not significant; such indications may be used to perform asignificance score for each category of physiological data, relationshipof such category to prognostic labels, and/or category of prognosticlabels is or is not significant per type of physiological sample, whichthen may be subjected to ranking, comparison to thresholds and/orelimination as described above.

Still referring to FIG. 1, diagnostic engine 104 may detect furthersignificant categories of physiological data, relationships of suchcategories to prognostic labels, and/or categories of prognostic labelsusing machine-learning processes, including without limitationunsupervised machine-learning processes as described in further detailbelow; such newly identified categories, as well as categories enteredby experts in free-form fields as described above, may be added topre-populated lists of categories, lists used to identify languageelements for language learning module, and/or lists used to identifyand/or score categories detected in documents, as described above.

Continuing to refer to FIG. 1, in an embodiment, diagnostic engine 104may be configured, for instance as part of receiving the first trainingset 106, to associate at least correlated first prognostic label 110with at least a category from a list of significant categories ofprognostic labels. Significant categories of prognostic labels may beacquired, determined, and/or ranked as described above. As anon-limiting example, prognostic labels may be organized according torelevance to and/or association with a list of significant conditions. Alist of significant conditions may include, without limitation,conditions having generally acknowledged impact on longevity and/orquality of life; this may be determined, as a non-limiting example, by aproduct of relative frequency of a condition within the population withyears of life and/or years of able-bodied existence lost, on average, asa result of the condition. A list of conditions may be modified for agiven person to reflect a family history of the person; for instance, aperson with a significant family history of a particular condition orset of conditions, or a genetic profile having a similarly significantassociation therewith, may have a higher probability of developing suchconditions than a typical person from the general population, and as aresult diagnostic engine 104 may modify list of significant categoriesto reflect this difference.

Still referring to FIG. 1, diagnostic engine 104 is designed andconfigured to receive a second training set 116 including a plurality ofsecond data entries. Each second data entry of the second training set116 includes at least a second prognostic label 118; at least a secondprognostic label 118 may include any label suitable for use as at leasta first prognostic label 110 as described above. Each second data entryof the second training set 116 includes at least an ameliorative processlabel 120 correlated with the at least a second prognostic label 118,where correlation may include any correlation suitable for correlationof at least a first prognostic label 110 to at least an element ofphysiological data as described above. As used herein, an ameliorativeprocess label 120 is an identifier, which may include any form ofidentifier suitable for use as a prognostic label as described above,identifying a process that tends to improve a physical condition of auser, where a physical condition of a user may include, withoutlimitation, any physical condition identifiable using a prognosticlabel. Ameliorative processes may include, without limitation, exerciseprograms, including amount, intensity, and/or types of exerciserecommended. Ameliorative processes may include, without limitation,dietary or nutritional recommendations based on data includingnutritional content, digestibility, or the like. Ameliorative processesmay include one or more medical procedures. Ameliorative processes mayinclude one or more physical, psychological, or other therapies.Ameliorative processes may include one or more medications. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various processes that may be used as ameliorative processesconsistently with this disclosure.

Continuing to refer to FIG. 1, in an embodiment diagnostic engine 104may be configured, for instance as part of receiving second training set116, to associate the at least second prognostic label 118 with at leasta category from a list of significant categories of prognostic labels.This may be performed as described above for use of lists of significantcategories with regard to at least a first prognostic label 110.Significance may be determined, and/or association with at least acategory, may be performed for prognostic labels in first training set106 according to a first process as described above and for prognosticlabels in second training set 116 according to a second process asdescribed above.

Still referring to FIG. 1, diagnostic engine 104 may be configured, forinstance as part of receiving second training set 116, to associate atleast a correlated ameliorative process label 120 with at least acategory from a list of significant categories of ameliorative processlabels 120. In an embodiment, diagnostic engine 104 and/or a user deviceconnected to diagnostic engine 104 may provide a second graphical userinterface 122 which may include without limitation a form or othergraphical element having data entry fields, wherein one or more experts,including without limitation clinical and/or scientific experts, mayenter information describing one or more categories of prognostic labelsthat the experts consider to be significant as described above; fieldsin graphical user interface may provide options describing previouslyidentified categories, which may include a comprehensive ornear-comprehensive list of types of prognostic labels, for instance in“drop-down” lists, where experts may be able to select one or moreentries to indicate their usefulness and/or significance in the opinionof the experts. Fields may include free-form entry fields such astext-entry fields where an expert may be able to type or otherwise entertext, enabling expert to propose or suggest categories not currentlyrecorded. Graphical user interface or the like may include fieldscorresponding to ameliorative labels, where experts may enter datadescribing ameliorative labels and/or categories of ameliorative labelsthe experts consider related to entered categories of prognostic labels;for instance, such fields may include drop-down lists or otherpre-populated data entry fields listing currently recorded ameliorativelabels, and which may be comprehensive, permitting each expert to selectan ameliorative label and/or a plurality of ameliorative labels theexpert believes to be predicted and/or associated with each category ofprognostic labels selected by the expert. Fields for entry ofameliorative labels and/or categories of ameliorative labels may includefree-form data entry fields such as text entry fields; as describedabove, examiners may enter data not presented in pre-populated datafields in the free-form data entry fields. Alternatively oradditionally, fields for entry of ameliorative labels may enable anexpert to select and/or enter information describing or linked to acategory of ameliorative label that the expert considers significant,where significance may indicate likely impact on longevity, mortality,quality of life, or the like as described in further detail below.Graphical user interface may provide an expert with a field in which toindicate a reference to a document describing significant categories ofprognostic labels, relationships of such categories to ameliorativelabels, and/or significant categories of ameliorative labels. Suchinformation may alternatively be entered according to any other suitablemeans for entry of expert data as described above. Data concerningsignificant categories of prognostic labels, relationships of suchcategories to ameliorative labels, and/or significant categories ofameliorative labels may be entered using analysis of documents usinglanguage processing module 114 or the like as described above.

In an embodiment, and still referring to FIG. 1, diagnostic engine 104may extract at least a second data entry from one or more documents;extraction may be performed using any language processing method asdescribed above. Diagnostic engine 104 may be configured, for instanceas part of receiving second training set 116, to receive at least adocument describing at least a medical history and extract at least asecond data entry of plurality of second data entries from the at leasta document. A medical history document may include, for instance, adocument received from an expert and/or medical practitioner describingtreatment of a patient; document may be anonymized by removal of one ormore patient-identifying features from document. A medical historydocument may include a case study, such as a case study published in amedical journal or written up by an expert. A medical history documentmay contain data describing and/or described by a prognostic label; forinstance, the medical history document may list a diagnosis that amedical practitioner made concerning the patient, a finding that thepatient is at risk for a given condition and/or evinces some precursorstate for the condition, or the like. A medical history document maycontain data describing and/or described by an ameliorative processlabel 120; for instance, the medical history document may list atherapy, recommendation, or other ameliorative process that a medicalpractitioner described or recommended to a patient. A medical historydocument may describe an outcome; for instance, medical history documentmay describe an improvement in a condition describing or described by aprognostic label, and/or may describe that the condition did notimprove. Prognostic labels, ameliorative process labels 120, and/orefficacy of ameliorative process labels 120 may be extracted from and/ordetermined from one or more medical history documents using anyprocesses for language processing as described above; for instance,language processing module 114 may perform such processes. As anon-limiting example, positive and/or negative indications regardingameliorative processes identified in medical history documents may bedetermined in a manner described above for determination of positiveand/or negative indications regarding categories of physiological data,relationships of such categories to prognostic labels, and/or categoriesof prognostic labels.

With continued reference to FIG. 1, diagnostic engine 104 may beconfigured, for instance as part of receiving second training set 116,to receiving at least a second data entry of the plurality of seconddata entries from at least an expert. This may be performed, withoutlimitation using second graphical user interface as described above.

Continuing to refer to FIG. 1, diagnostic engine 104 may be configuredto record at least a biological extraction. At least a biologicalextraction may include any element and/or elements of data suitable foruse as at least an element of physiological state data as describedabove. At least a biological extraction may include a physicallyextracted sample, which as used herein, includes a sample obtained byremoving and analyzing tissue and/or fluid. Physically extracted samplemay include without limitation a blood sample, a tissue sample, a buccalswab, a mucous sample, a stool sample, a hair sample, a fingernailsample, or the like. Physically extracted sample may include, as anon-limiting example, at least a blood sample. As a further non-limitingexample, at least a biological extraction may include at least a geneticsample. At least a genetic sample may include a complete genome of aperson or any portion thereof. At least a genetic sample may include aDNA sample and/or an RNA sample. At least a biological extraction mayinclude an epigenetic sample, a proteomic sample, a tissue sample, abiopsy, and/or any other physically extracted sample. At least abiological extraction may include an endocrinal sample. As a furthernon-limiting example, the at least a biological extraction may include asignal from at least a sensor configured to detect physiological data ofa user and recording the at least a biological extraction as a functionof the signal. At least a sensor 124 may include any medical sensorand/or medical device configured to capture sensor data concerning apatient, including any scanning, radiological and/or imaging device suchas without limitation x-ray equipment, computer assisted tomography(CAT) scan equipment, positron emission tomography (PET) scan equipment,any form of magnetic resonance imagery (MRI) equipment, ultrasoundequipment, optical scanning equipment such as photo-plethysmographicequipment, or the like. At least a sensor 124 may include anyelectromagnetic sensor, including without limitationelectroencephalographic sensors, magnetoencephalographic sensors,electrocardiographic sensors, electromyographic sensors, or the like. Atleast a sensor 124 may include a temperature sensor. At least a sensor124 may include any sensor that may be included in a mobile deviceand/or wearable device, including without limitation a motion sensorsuch as an inertial measurement unit (IMU), one or more accelerometers,one or more gyroscopes, one or more magnetometers, or the like. At leasta wearable and/or mobile device sensor may capture step, gait, and/orother mobility data, as well as data describing activity levels and/orphysical fitness. At least a wearable and/or mobile device sensor maydetect heart rate or the like. At least a sensor 124 may detect anyhematological parameter including blood oxygen level, pulse rate, heartrate, pulse rhythm, blood sugar, and/or blood pressure. At least asensor 108 may be configured to detect internal and/or externalbiomarkers and/or readings. At least a sensor 124 may be a part ofsystem 100 or may be a separate device in communication with system 100.

Still referring to FIG. 1, at least a biological extraction may includeany data suitable for use as physiological state data as describedabove, including without limitation any result of any medical test,physiological assessment, cognitive assessment, psychologicalassessment, or the like. System 100 may receive at least a biologicalextraction from one or more other devices after performance; system 100may alternatively or additionally perform one or more assessments and/ortests to obtain at least a biological extraction, and/or one or moreportions thereof, on system 100. For instance, at least biologicalextraction may include or more entries by a user in a form or similargraphical user interface object; one or more entries may include,without limitation, user responses to questions on a psychological,behavioral, personality, or cognitive test. For instance, a computingdevice 102 may present to user a set of assessment questions designed orintended to evaluate a current state of mind of the user, a currentpsychological state of the user, a personality trait of the user, or thelike; a computing device 102 may provide user-entered responses to suchquestions directly as at least a biological extraction and/or mayperform one or more calculations or other algorithms to derive a scoreor other result of an assessment as specified by one or more testingprotocols, such as automated calculation of a Stanford-Binet and/orWechsler scale for IQ testing, a personality test scoring such as aMyers-Briggs test protocol, or other assessments that may occur topersons skilled in the art upon reviewing the entirety of thisdisclosure.

With continued reference to FIG. 1, at least a biological extraction mayinclude assessment and/or self-assessment data, and/or automated orother assessment results, obtained from a third-party device;third-party device may include, without limitation, a server or otherdevice (not shown) that performs automated cognitive, psychological,behavioral, personality, or other assessments. Third-party device mayinclude a device operated by an informed advisor.

Still referring to FIG. 1, at least a biological extraction may includedata describing one or more test results, including results of mobilitytests, stress tests, dexterity tests, endocrinal tests, genetic tests,and/or electromyographic tests, biopsies, radiological tests, genetictests, and/or sensory tests. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various additionalexamples of at least a physiological sample consistent with thisdisclosure. At least a physiological sample may be added to biologicalextraction database 200.

With continued reference to FIG. 1, system 100 may include a prognosticlabel learner 126 operating on the diagnostic engine 104, the prognosticlabel learner 126 designed and configured to generate the at least aprognostic output as a function of the first training set 106 and the atleast a biological extraction. Prognostic label learner 126 may includeany hardware and/or software module. Prognostic label learner 126 isdesigned and configured to generate outputs using machine learningprocesses. A machine learning process is a process that automatedly usesa body of data known as “training data” and/or a “training set” togenerate an algorithm that will be performed by a computingdevice/module to produce outputs given data provided as inputs; this isin contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language.

Still referring to FIG. 1, prognostic label learner 126 may be designedand configured to generate at least a prognostic output by creating atleast a first machine-learning model 128 relating physiological statedata 108 to prognostic labels using the first training set 106 andgenerating the at least a prognostic output using the firstmachine-learning model 128; at least a first machine-learning model 128may include one or more models that determine a mathematicalrelationship between physiological state data 108 and prognostic labels.Such models may include without limitation model developed using linearregression models. Linear regression models may include ordinary leastsquares regression, which aims to minimize the square of the differencebetween predicted outcomes and actual outcomes according to anappropriate norm for measuring such a difference (e.g. a vector-spacedistance norm); coefficients of the resulting linear equation may bemodified to improve minimization. Linear regression models may includeridge regression methods, where the function to be minimized includesthe least-squares function plus term multiplying the square of eachcoefficient by a scalar amount to penalize large coefficients. Linearregression models may include least absolute shrinkage and selectionoperator (LASSO) models, in which ridge regression is combined withmultiplying the least-squares term by a factor of 1 divided by doublethe number of samples. Linear regression models may include a multi-tasklasso model wherein the norm applied in the least-squares term of thelasso model is the Frobenius norm amounting to the square root of thesum of squares of all terms. Linear regression models may include theelastic net model, a multi-task elastic net model, a least angleregression model, a LARS lasso model, an orthogonal matching pursuitmodel, a Bayesian regression model, a logistic regression model, astochastic gradient descent model, a perceptron model, a passiveaggressive algorithm, a robustness regression model, a Huber regressionmodel, or any other suitable model that may occur to persons skilled inthe art upon reviewing the entirety of this disclosure. Linearregression models may be generalized in an embodiment to polynomialregression models, whereby a polynomial equation (e.g. a quadratic,cubic or higher-order equation) providing a best predicted output/actualoutput fit is sought; similar methods to those described above may beapplied to minimize error functions, as will be apparent to personsskilled in the art upon reviewing the entirety of this disclosure.

With continued reference to FIG. 1, machine-learning algorithms maygenerate prognostic output as a function of a classification of at leasta prognostic label. Classification as used herein includes pairing orgrouping prognostic labels as a function of a shared commonality.Classification may include for example, groupings, pairings, and/ortrends between physiological data and current prognostic label, futureprognostic label, and the like. In an embodiment, machine-learningalgorithms may examine relationships between a future propensity of auser to develop a condition based on current user physiological data.Machine-learning algorithms may include any and all algorithms asperformed by any modules, described herein for prognostic label learner126. For example, machine-learning algorithms may relate fasting bloodglucose readings of a user to user's future propensity to developdiabetes. Machine-learning algorithms may examine precursor conditionand future propensity to develop a subsequent disorder. For example,machine-learning algorithms may examine a user diagnosed with chickenpox and user's future propensity to subsequently develop shingles. Inyet another non-limiting example, machine-learning algorithms mayexamine infection with human papillomavirus (HPV) and subsequent cancerdiagnosis. Machine-learning algorithms may examine a user's propensityto have recurring attacks of a disease or condition, for example a userwith elevated uric acid levels and repeated attacks of gout.Machine-learning algorithms may examine user's genetic predisposition todevelop a certain condition or disease. For example, machine-learningalgorithms may examine presence of hereditary non-polyposis colorectalcancer (HNPCC) commonly known as lynch syndrome, and subsequentdiagnosis of colorectal cancer. In yet another non-limiting example,machine-learning algorithms may examine presence of abnormal squamouscells and/or abnormal glandular cells in the cervix and subsequentdevelopment of cervical cancer. Machine-learning algorithms may examineprogression of disease state, for example progression of humanimmunodeficiency virus (HIV) is marked by decline of CD4+T-Cells, with acount below 200 leading to a diagnosis of acquired immunodeficiencysyndrome (AIDS). In yet another non-limiting example, progression ofdiabetes may be marked by increases of hemoglobin A1C levels with alevel of 6.5% indicating a diagnosis of diabetes. Machine-learningalgorithms may examine progression of disease by certain age groups. Forexample, progression of Multiple Sclerosis in users between the age of20-30 as compared to progression of Multiple Sclerosis in users betweenthe age of 70-80. Machine-learning algorithms may be examiningprogression of aging such as measurements of telomere length and/oroxidative stress levels and chance mortality risk. Machine-learningalgorithms may examine development of co-morbid conditions when adisease or conditions is already present. For example, machine-learningalgorithms may examine a user diagnosed with depression and subsequentdiagnosis of a co-morbid condition such as migraines, generalizedanxiety disorder, antisocial personality disorder, agoraphobia,obsessive-compulsive disorder, drug dependence alcohol dependence,and/or panic disorder. Machine-learning algorithms may examine a user'slifetime chance of developing a certain disease or condition, such as auser's lifetime risk of heart disease, Alzheimer's disease, diabetes andthe like. Machine-learning algorithms may be grouped and implementedaccording to any of the methodologies as described below in reference toFIG. 19.

Continuing to refer to FIG. 1, machine-learning algorithm used togenerate first machine-learning model 128 may include, withoutlimitation, linear discriminant analysis. Machine-learning algorithm mayinclude quadratic discriminate analysis. Machine-learning algorithms mayinclude kernel ridge regression. Machine-learning algorithms may includesupport vector machines, including without limitation support vectorclassification-based regression processes. Machine-learning algorithmsmay include stochastic gradient descent algorithms, includingclassification and regression algorithms based on stochastic gradientdescent. Machine-learning algorithms may include nearest neighbors'algorithms. Machine-learning algorithms may include Gaussian processessuch as Gaussian Process Regression. Machine-learning algorithms mayinclude cross-decomposition algorithms, including partial least squaresand/or canonical correlation analysis. Machine-learning algorithms mayinclude naïve Bayes methods. Machine-learning algorithms may includealgorithms based on decision trees, such as decision tree classificationor regression algorithms. Machine-learning algorithms may includeensemble methods such as bagging meta-estimator, forest of randomizedtress, AdaBoost, gradient tree boosting, and/or voting classifiermethods. Machine-learning algorithms may include neural net algorithms,including convolutional neural net processes.

Still referring to FIG. 1, prognostic label learner 126 may generateprognostic output using alternatively or additional artificialintelligence methods, including without limitation by creating anartificial neural network, such as a convolutional neural networkcomprising an input layer of nodes, one or more intermediate layers, andan output layer of nodes. Connections between nodes may be created viathe process of “training” the network, in which elements from a trainingdataset are applied to the input nodes, a suitable training algorithm(such as Levenberg-Marquardt, conjugate gradient, simulated annealing,or other algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning. This network may be trained using first trainingset 106; the trained network may then be used to apply detectedrelationships between elements of physiological state data 108 andprognostic labels.

With continued reference to FIG. 1, machine-learning algorithms mayinclude unsupervised processes; unsupervised processes may, as anon-limiting example, be executed by an unsupervised learning moduleexecuting on diagnostic engine 104 and/or on another computing device incommunication with diagnostic engine 104, which may include any hardwareor software module as described in more detail below in reference toFIG. 7. An unsupervised machine-learning process, as used herein, is aprocess that derives inferences in datasets without regard to labels; asa result, an unsupervised machine-learning process may be free todiscover any structure, relationship, and/or correlation provided in thedata. For instance, and without limitation, prognostic label learner 126and/or diagnostic engine 104 may perform an unsupervised machinelearning process on first training set 106, which may cluster data offirst training set 106 according to detected relationships betweenelements of the first training set 106, including without limitationcorrelations of elements of physiological state data 108 to each otherand correlations of prognostic labels to each other; such relations maythen be combined with supervised machine learning results to add newcriteria for prognostic label learner 126 to apply in relatingphysiological state data 108 to prognostic labels. As a non-limiting,illustrative example, an unsupervised process may determine that a firstelement of physiological data acquired in a blood test correlatesclosely with a second element of physiological data, where the firstelement has been linked via supervised learning processes to a givenprognostic label, but the second has not; for instance, the secondelement may not have been defined as an input for the supervisedlearning process, or may pertain to a domain outside of a domainlimitation for the supervised learning process. Continuing the example aclose correlation between first element of physiological state data 108and second element of physiological state data 108 may indicate that thesecond element is also a good predictor for the prognostic label; secondelement may be included in a new supervised process to derive arelationship or may be used as a synonym or proxy for the firstphysiological element by prognostic label learner 126.

Still referring to FIG. 1, diagnostic engine 104 and/or prognostic labellearner 126 may detect further significant categories of physiologicaldata, relationships of such categories to prognostic labels, and/orcategories of prognostic labels using machine-learning processes,including without limitation unsupervised machine-learning processes asdescribed above; such newly identified categories, as well as categoriesentered by experts in free-form fields as described above, may be addedto pre-populated lists of categories, lists used to identify languageelements for language learning module, and/or lists used to identifyand/or score categories detected in documents, as described above. In anembodiment, as additional data is added to system 100, prognostic labellearner 126 and/or diagnostic engine 104 may continuously or iterativelyperform unsupervised machine-learning processes to detect relationshipsbetween different elements of the added and/or overall data; in anembodiment, this may enable system 100 to use detected relationships todiscover new correlations between known biomarkers, prognostic labels,and/or ameliorative labels and one or more elements of data in largebodies of data, such as genomic, proteomic, and/or microbiome-relateddata, enabling future supervised learning and/or lazy learning processesas described in further detail below to identify relationships between,e.g., particular clusters of genetic alleles and particular prognosticlabels and/or suitable ameliorative labels. Use of unsupervised learningmay greatly enhance the accuracy and detail with which system may detectprognostic labels and/or ameliorative labels.

With continued reference to FIG. 1, unsupervised processes may besubjected to domain limitations. For instance, and without limitation,an unsupervised process may be performed regarding a comprehensive setof data regarding one person, such as a comprehensive medical history,set of test results, and/or physiological data such as genomic,proteomic, and/or other data concerning that persons. As anothernon-limiting example, an unsupervised process may be performed on dataconcerning a particular cohort of persons; cohort may include, withoutlimitation, a demographic group such as a group of people having ashared age range, ethnic background, nationality, sex, and/or gender.Cohort may include, without limitation, a group of people having ashared value for an element and/or category of physiological data, agroup of people having a shared value for an element and/or category ofprognostic label, and/or a group of people having a shared value and/orcategory of ameliorative label; as illustrative examples, cohort couldinclude all people having a certain level or range of levels of bloodtriglycerides, all people diagnosed with type II diabetes, all peoplewho regularly run between 10 and 15 miles per week, or the like. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of a multiplicity of ways in which cohorts and/or other sets ofdata may be defined and/or limited for a particular unsupervisedlearning process.

Still referring to FIG. 1, prognostic label learner 126 mayalternatively or additionally be designed and configured to generate atleast a prognostic output by executing a lazy learning process as afunction of the first training set 106 and the at least a biologicalextraction; lazy learning processes may be performed by a lazy learningmodule executing on diagnostic engine 104 and/or on another computingdevice in communication with diagnostic engine 104, which may includeany hardware or software module. A lazy-learning process and/orprotocol, which may alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover a “first guess” ata prognostic label associated with biological extraction, using firsttraining set 106. As a non-limiting example, an initial heuristic mayinclude a ranking of prognostic labels according to relation to a testtype of at least a biological extraction, one or more categories ofphysiological data identified in test type of at least a biologicalextraction, and/or one or more values detected in at least a biologicalextraction; ranking may include, without limitation, ranking accordingto significance scores of associations between elements of physiologicaldata and prognostic labels, for instance as calculated as describedabove. Heuristic may include selecting some number of highest-rankingassociations and/or prognostic labels. Prognostic label learner 126 mayalternatively or additionally implement any suitable “lazy learning”algorithm, including without limitation a K-nearest neighbors algorithm,a lazy naïve Bayes algorithm, or the like; persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variouslazy-learning algorithms that may be applied to generate prognosticoutputs as described in this disclosure, including without limitationlazy learning applications of machine-learning algorithms as describedin further detail below.

Continuing to refer to FIG. 1, prognostic label learner 126 may generatea plurality of prognostic labels having different implications for aparticular person. For instance, where the at least a physiologicalsample includes a result of a dexterity test, a low score may beconsistent with amyotrophic lateral sclerosis, Parkinson's disease,multiple sclerosis, and/or any number of less sever disorders ortendencies associated with lower levels of dexterity. In such asituation, prognostic label learner 126 and/or diagnostic engine 104 mayperform additional processes to resolve ambiguity. Processes may includepresenting multiple possible results to a medical practitioner,informing the medical practitioner that one or more follow-up testsand/or physiological samples are needed to further determine a moredefinite prognostic label. Alternatively or additionally, processes mayinclude additional machine learning steps; for instance, where referenceto a model generated using supervised learning on a limited domain hasproduced multiple mutually exclusive results and/or multiple resultsthat are unlikely all to be correct, or multiple different supervisedmachine learning models in different domains may have identifiedmutually exclusive results and/or multiple results that are unlikely allto be correct. In such a situation, prognostic label learner 126 and/ordiagnostic engine 104 may operate a further algorithm to determine whichof the multiple outputs is most likely to be correct; algorithm mayinclude use of an additional supervised and/or unsupervised model.Alternatively or additionally, prognostic label learner 126 may performone or more lazy learning processes using a more comprehensive set ofuser data to identify a more probably correct result of the multipleresults. Results may be presented and/or retained with rankings, forinstance to advise a medical professional of the relative probabilitiesof various prognostic labels being correct; alternatively oradditionally, prognostic labels associated with a probability ofcorrectness below a given threshold and/or prognostic labelscontradicting results of the additional process, may be eliminated. As anon-limiting example, an endocrinal test may determine that a givenperson has high levels of dopamine, indicating that a poor pegboardperformance is almost certainly not being caused by Parkinson's disease,which may lead to Parkinson's being eliminated from a list of prognosticlabels associated with poor pegboard performance, for that person.Similarly, a genetic test may eliminate Huntington's disease, or anotherdisease definitively linked to a given genetic profile, as a cause.Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various ways in which additional processingmay be used to determine relative likelihoods of prognostic labels on alist of multiple prognostic labels, and/or to eliminate some labels fromsuch a list. Prognostic output 712 may be provided to user output deviceas described in further detail below.

Still referring to FIG. 1, diagnostic engine 104 includes anameliorative process label learner 130 operating on the diagnosticengine 104, the ameliorative process label learner 130 designed andconfigured to generate the at least an ameliorative output as a functionof the second training set 116 and the at least a prognostic output.Ameliorative process label learner 130 may include any hardware orsoftware module suitable for use as a prognostic label learner 126 asdescribed above. Ameliorative process label learner 130 is amachine-learning module as described above; ameliorative process labellearner 130 may perform any machine-learning process or combination ofprocesses suitable for use by a prognostic label learner 126 asdescribed above. For instance, and without limitation, and ameliorativeprocess label learner 130 may be configured to create a secondmachine-learning model 132 relating prognostic labels to ameliorativelabels using the second training set 116 and generate the at least anameliorative output using the second machine-learning model 132; secondmachine-learning model 132 may be generated according to any process,process steps, or combination of processes and/or process steps suitablefor creation of first machine learning model. In an embodiment,ameliorative process label learner 130 may use data from first trainingset 106 as well as data from second training set 116; for instance,ameliorative process label learner 130 may use lazy learning and/ormodel generation to determine relationships between elements ofphysiological data, in combination with or instead of prognostic labels,and ameliorative labels. Where ameliorative process label learner 130determines relationships between elements of physiological data andameliorative labels directly, this may determine relationships betweenprognostic labels and ameliorative labels as well owing to the existenceof relationships determined by prognostic label learner 126.

With continued reference to FIG. 1, diagnostic engine 104 may include analimentary instruction label learner 134 operating on the diagnosticengine 104, the alimentary instruction label learner 134 designed andconfigured to generate at least an alimentary data output as a functionof the second training set 116 and the at least a prognostic output.Alimentary instruction label learner 134 may include any hardware orsoftware module suitable for use as a prognostic label learner 126 asdescribed above. Alimentary instruction label learner 134 may include amachine-learning module as described above; alimentary instruction labellearner 134 may perform any machine-learning process or combination ofprocesses suitable for use by a prognostic label learner 126 asdescribed above. For instance, and without limitation, and alimentaryinstruction label learner 134 may be configured to create a thirdmachine-learning model 136 relating prognostic labels to alimentarylabels using the second training set 116 and generate the at least analimentary data output using the third machine-learning model 136; thirdmachine-learning model 136 may be generated according to any process,process steps, or combination of processes and/or process steps suitablefor creation of first machine learning model. In an embodiment,alimentary instruction label learner 134 may use data from firsttraining set 106 as well as data from second training set 116; forinstance, alimentary instruction label learner 134 may use lazy learningand/or model generation to determine relationships between elements ofphysiological data, in combination with or instead of prognostic labels,and alimentary labels, which may include, without limitation, a subsetof ameliorative labels corresponding to alimentary processes. Wherealimentary instruction label learner 134 determines relationshipsbetween elements of physiological data and alimentary labels directly,this may determine relationships between prognostic labels andalimentary labels as well owing to the existence of relationshipsdetermined by prognostic label learner 126.

With continued reference to FIG. 1, system 100 includes a plan generatormodule 138 operating on a computing device 102. Plan generator module138 may include any suitable hardware or hardware module. In anembodiment, plan generator module 138 is designed and configured togenerate a comprehensive instruction set associated with the user as afunction of the diagnostic output. In an embodiment, comprehensiveinstruction set 140 is a data structure containing instructions to beprovided to the user to explain the user's current prognostic status, asreflected by one or more prognostic outputs and provide the user with aplan based on the at least an ameliorative output, to achieve that. Inan embodiment, comprehensive instruction set 140 may be generated basedon at least an informed advisor output. Comprehensive instruction set140 may include but is not limited to a program, strategy, summary,recommendation, or any other type of interactive platform that may beconfigured to comprise information associated with the user, anapplicable verified external source, and one or more outputs derivedfrom the analyses performed on the extraction from the user.Comprehensive instruction set 140 may describe to a user a futureprognostic status to aspire to. Comprehensive instruction set 140 mayreflect analyses and diagnostics associated with a user.

With continued reference to FIG. 1, system 100 includes an alimentaryinstruction set module 142 operating on a computing device 102.Alimentary instruction set module 142 may include any suitable hardwareor hardware module. In an embodiment, alimentary instruction set moduleis designed and configured to generate as a function of thecomprehensive instruction set an alimentary instruction set associatedwith the user. In an embodiment, alimentary instruction set 144 is adata structure containing a solution and/or suggestion to nourishmentrequirements or constitutional or chemical deficiencies. Alimentaryinstruction set 144 may be generated as a function of comprehensiveinstruction set 140. For example, comprehensive instruction set 140 thatcontains a recommendation to increase iron intake based on at least abiological extraction from a user reflecting anemia, may be utilized togenerate an alimentary instruction set that includes a suggestion for auser to increase consumption of organ meats and green leafy vegetables.In yet another non-limiting example, alimentary instruction set 144 maycontain a component seeking to remedy a B vitamin deficiency of a userbased on a comprehensive instruction set 140 showing blood levels of Bvitamins below normal acceptable values.

With continued reference to FIG. 1, alimentary instruction set 144 maybe generated upon receiving at least an element of including aconstitutional restriction. Element of user data as used herein, is anyelement of data describing the user, user needs, and/or userpreferences. At least an element of user data may include aconstitutional restriction. At least a constitutional restriction mayinclude any constitutional reason that a user may be unable to engage inan alimentary instruction set process; at least a constitutionalrestriction may include a contraindication such as an injury, adiagnosis such as by an informed advisor including a functional medicinedoctor, an allergy or food sensitivity issue, a contraindication to amedication or supplement and the like. For example, a user diagnosedwith a blood clot and currently taking a blood thinning medication suchas warfarin may report a constitutional restriction that includes a needto eat a consistent amount of Vitamin K containing foods such as leafygreens each day.

With continued reference to FIG. 1, alimentary instruction set may begenerated upon receiving at least an element of user data including atleast a user preference. At least a user preference may include, withoutlimitation, any preference to engage in or eschew any alimentaryinstruction set process and/or other potential elements of comprehensiveinstruction set 140. At least a user preference may include for examplereligious preferences such as forbidden foods, medical interventions,exercise routines and the like. At least a user preference may include auser's dislike such as for example a user aversion to certain foods ornutrient groups, such as for example an aversion to eggs or an aversionto beets. At least a user preference may include for example a user'slikes such as a user's preference to consume animal products or full fatdairy and the like. In an embodiment, alimentary instruction set 144 maybe transmitted by alimentary instruction set module 142 to a user suchas to a user client device 156, utilizing any of the transmissionmethodologies as described herein any network transmissions.

With continued reference to FIG. 1, alimentary instruction set module142 may be configured to transmit a self-fulfillment instruction set toa user such as to user client device 156. Transmission may occurutilizing any of the transmission methodologies as described hereinincluding any network transmissions. A “self-fulfillment instructionset” as used herein, is a data structure containing suggestions to beprovided to the user to explain different ways in which a user canself-fulfill alimentary instruction set 144. Self-fulfillmentinstruction set 146 may contain suggestions as to foods and/or mealsthat a user may consume to correct nutrient and/or chemicaldeficiencies. Self-fulfillment instruction set 146 may be generated as afunction of user geolocation. A user geolocation, as used in thisdisclosure, is an identification of a real-world geographical locationof a user. A user geolocation may be obtained from a radar source, userclient device such as a mobile phone, and/or internet connected devicelocation. A user geolocation may include a global positioning system(GPS) of a system. A user geolocation may include geographic coordinatesthat specify the latitude and longitude where a user is located. Userlocation including geographic location of a user may be utilized togenerate a self-fulfillment instruction set that may contain ingredientsor selections that may be available to a user in a certain geographicallocation. For example, a user with an alimentary instruction set thatcontains a deficiency of essential fatty acids may receive aself-fulfillment instruction set 146 that contains suggestions as toincreasing one's consumption of salmon, herring, and cod. In anembodiment, self-fulfillment instruction set may be generated as afunction of geolocation of a user. For example, a user with an essentialfatty acid deficiency who is located in Seattle, Washington may receivea self-fulfillment instruction set 146 to increase consumption oflocally available wild fish such as yellow perch, walleye, and stripedbass, while a user with an essential fatty acid deficiency who islocated in Naples, Florida may receive a self-fulfillment instructionset to increase one's consumption of red snapper, black grouper, andFlorida pompano. In an embodiment, self-fulfillment instruction set 146may include a plurality of different suggestions as to ways in whichuser can self-fulfill alimentary instruction set 144. For example,self-fulfillment instruction set may include suggested recipes a usermay wish to cook, suggested groceries a user may wish to purchase,suggested meals a user may wish to consume, suggested meal plans a usermay wish to follow, suggested eating habits a user may wish to follow,suggested restaurants a user may wish to eat at and the like. In anembodiment, self-fulfillment instruction set may include suggestionsbased on user location. For example, user may receive a suggestedgrocery list based on grocery stores in user's area where user isphysically present.

With continued reference to FIG. 1, alimentary instruction set module142 may include self-fulfillment learner 148. Self-fulfillment learner148 may contain any hardware or software module suitable for use asprognostic label learner 126 as described above. Self-fulfillmentlearner 148 may include a machine-learning module as described above,self-fulfillment learner may perform any machine-learning process orcombination of processes suitable for use by prognostic label learner126 as described above. For instance and without limitation,self-fulfillment learner 148 may be configured to create a fourthmachine-learning model 150 relating self-fulfillment instruction sets toameliorative process labels and/or user entries containing an alimentaryself-fulfillment action. An alimentary self-fulfillment action, is datadescribing how a user self-fulfilled. Fourth machine-learning model 150may be generated according to any process, process steps, or combinationof processes and/or process steps suitable for creation of firstmachine-learning model. In an embodiment, self-fulfillment learner 148may use data from first training set 106, second training set 116; forinstance, self-fulfillment learner 148 may use lazy learning and/ormodel generation to determine relationships between elements ofphysiological data, in combination with or instead of prognostic labels,and alimentary labels, which may include, without limitation, a subsetof self-fulfillment labels corresponding to self-fulfillment actions.For example, user entry, as described in more detail below, may containa description pertaining to how user self-fulfilled an alimentaryinstruction set, such as by shopping for groceries at a local grocerystore. Subsequent self-fulfillment instruction sets 146 may be generatedbased on trends and data collected from user entries. User entries thatcontain trends and/or repeat habits established by a user may beutilized in machine-learning algorithms to generate subsequentself-fulfillment instruction sets 146. For example, a user entry thatcontains self-fulfillment actions that include actions such as cookingmeals at home may be utilized to generate subsequent self-fulfillmentinstruction sets that focuses on new recipes as opposed to potentialrestaurants a user may want to visit. In yet another example, a userentry that contains self-fulfillment actions such as ordering takeoutfrom a restaurant may be utilized to generate subsequentself-fulfillment instruction sets that may not focus on new recipes orgrocery shopping lists but instead may focus on different restaurants auser may want to try.

With continued reference to FIG. 1, self-fulfillment learner 148 mayperform machine-learning algorithms using a loss function analysisutilizing linear regression based on past interactions between a userand system 100 and self-fulfillment instruction sets to generateself-fulfillment instruction sets. In an embodiment, self-fulfillmentlearner 148 may compare one or more self-fulfillment options to amathematical expression representing an optimal combination ofself-fulfillment variables. Mathematical expression may include a linearcombination of variables, weighted by coefficients representing relativeimportance of each variables in generating an optimal self-fulfillmentaction. For instance, a variable such as total transit time in secondsof a self-fulfillment action may be multiplied by a first coefficientrepresenting the importance of total transit time, a total cost of aself-fulfillment action may be multiplied by a second coefficientrepresenting the importance of cost, a degree of variance from anself-fulfillment instruction set may be represented as anotherparameter, which may be multiplied by another coefficient representingthe importance of that parameter, a degree of variance from a requestedrecipe may be multiplied by an additional coefficient representing animportance of that parameter, and/or a parameter representing a degreeof variance from one or more dietary restrictions may be provided acoefficient representing the importance of such a variance; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of different variables that may be weighted by variouscoefficients. Use of a linear combination is provided only as anillustrative example; other mathematical expressions may alternativelyor additionally be used, including without limitation higher-orderpolynomial expressions or the like.

Still viewing FIG. 1, mathematical expression may represent a lossfunction, where a “loss function” is an expression an output of which anoptimization algorithm minimizes to generate an optimal result. As anon-limiting example, self-fulfillment learner may calculate variablesof each of a plurality of self-fulfillment actions, calculate an outputof mathematical expression using the variables, and select aself-fulfillment action that produces an output having the lowest size,according to a given definition of “size,” of the set of outputsrepresenting each of the plurality of self-fulfillment actions; sizemay, for instance, included absolute value, numerical size, or the like.Selection of different loss functions may result in identification ofdifferent self-fulfillment actions as generating minimal outputs; forinstance, where transit time is associated in a first loss function witha large coefficient or weight, a self-fulfillment action having a shorttransit time may minimize the first loss function, whereas a second lossfunction wherein transit time has a smaller coefficient but degree ofvariance from a dietary restriction has a larger coefficient may producea minimal output for a different self-fulfillment action having a longertransit time but more closely hewing to a dietary restriction.

Alternatively or additionally, and still referring to FIG. 1, eachself-fulfillment action may be represented by a mathematical expressionhaving the same form as mathematical expression; self-fulfillmentlearner 148 may compare the former to the latter using an error functionrepresenting average difference between the two mathematicalexpressions. Error function may, as a non-limiting example, becalculated using the average difference between coefficientscorresponding to each variable. A self-fulfillment action having amathematical expression minimizing the error function may be selected,as representing an optimal expression of relative importance ofvariables to a system or user. In an embodiment, error function and lossfunction calculations may be combined; for instance, a self-fulfillmentaction resulting in a minimal aggregate expression of error function andloss function, such as a simple addition, arithmetic mean, or the likeof the error function with the loss function, may be selected,corresponding to an option that minimizes total variance from optimalvariables while simultaneously minimizing a degree of variance from aset of priorities corresponding to self-fulfillment action variables.Coefficients of mathematical expression and/or loss function may bescaled and/or normalized; this may permit comparison and/or errorfunction calculation to be performed without skewing by varied absolutequantities of numbers.

Still referring to FIG. 1, mathematical expression and/or loss functionmay be provided by receiving one or more user commands. For instance,and without limitation, a graphical user interface may be provided touser with a set of sliders or other user inputs permitting a user toindicate relative and/or absolute importance of each variable to theuser. Sliders or other inputs may be initialized prior to user entry asequal, or may be set to default values based on results of anymachine-learning processes or combinations thereof as described infurther detail below.

With continued reference to FIG. 1, mathematical expression and/or lossfunction may be generated using a machine learning to produce lossfunction: i.e., regression. Mathematical expression and/or loss functionbe user-specific, using a training set composed of past user selections;may be updated continuously. Mathematical expression and/or lossfunction may initially be seeded using one or more user entries asabove. User may enter a new command changing mathematical expression,and then subsequent user selections may be used to generate a newtraining set to modify the new expression.

With continued reference to FIG. 1, mathematical expression and/or lossfunction may be generated using machine learning using a multi-usertraining set. Training set may be created using data of a cohort ofpersons having similar demographic, religious, health, and/or lifestylecharacteristics to user. This may alternatively or additionally be usedto seed a mathematical expression and/or loss function for a user, whichmay be modified by further machine learning and/or regression usingsubsequent user selections of alimentary provision options.

Self-fulfillment learner may generate a loss function of user specificvariables and minimize the loss function. Self-fulfillment learner 148may generate self-fulfillment instruction set 146 utilizing lossfunction analysis. Loss function analysis may measure changes inpredicted values versus actual values, known as loss or error. Lossfunction analysis may utilize gradient descent to learn the gradient ordirection that a cost analysis should take in order to reduce errors.Loss function analysis algorithms may iterate to gradually convergetowards a minimum where further tweaks to the parameters produce littleor zero changes in the loss or convergence by optimizing weightsutilized by machine learning algorithms. Loss function analysis mayexamine the cost of the difference between estimated values, tocalculate the difference between hypothetical and real values.Self-fulfillment learner 148 may utilize variables to modelrelationships between past interactions between a user and system 100and self-fulfillment instruction sets. In an embodiment loss functionanalysis may utilize variables that may impact user interactions and/orself-fulfillment instruction sets. Variables may include user's habits,such as if user shops for groceries, how often user prepares meals athome, how often user eats out at restaurants or fast food stops, and thelike. Variables may include for example, product quality which mayinclude scores for a user's desire to consume organic or locally sourcedingredients. Variables may include for example product ingredients whichmay include scores for how different products may fulfill a user'salimentary instruction set needs, such as for example products that maycontain iron for a user with anemia. Variables may include cost such asfor example how much money a user is willing to pay for an ingredient orquality and how cost may factor into a user's overall budget for food.For example, a user with a fixed budget may be satisfied eating anonorganic apple and avoiding the apple core where the pesticides resideas compared to spending more money on an organic apple. Variables mayinclude travel time and geographical location such as for example howfar a user is willing to travel to a grocery store or restaurant toacquire ingredients or a meal. Variables may include a user preferencefor certain foods or food groups such as a user who doesn't wish toconsume foods containing monosodium glutamate (MSG) or a user who seeksto avoid trans fats. Variables may include user preferences such as auser's preference to consume user's favorite foods or meals. Variablesmay include availability of certain products and ingredients such as forexample, availability of fresh seafood in Denver, Colo. or availabilityof fresh avocados in Boston, Mass. Loss function analysis may be userspecific so as to create algorithms and outputs that are customize tovariables for an individual user. User behaviors and user past responsesmay be utilized as training data to generate outputs. Variablescontained within loss function analysis may be weighted and givendifferent numerical scores. Variables may be stored and utilized topredict subsequent outputs. Outputs may seek to predict user behaviorand past user interactions with system 100 and self-fulfillmentinstruction sets.

With continued reference to FIG. 1, system 100 includes fulfillmentmodule 152. Fulfillment module 152 may include any suitable hardware orhardware module. Fulfillment module 152 is configured to generate aself-fulfillment instruction set utilizing a diagnostic output.Self-fulfillment instruction set includes any of the self-fulfillmentinstruction sets as described above in more detail above. Fulfillmentmodule 152 is configured to generate a self-fulfillment instruction setutilizing a machine-learning model. Fulfillment module 152 is configuredto receive user training data. User training data, as used in thisdisclosure, is training data that contains a plurality of previous userentries containing previous user alimentary instruction sets and aplurality of correlated self-fulfillment instruction sets. User trainingdata is training data generated from previously generated alimentaryinstruction sets and self-fulfillment instruction sets generated for auser. In an embodiment, one or more previous user entries may be storedwithin variables database as described below in more detail. Fulfillmentmodule 152 generates a self-fulfillment instruction set utilizing usertraining data and a fulfillment machine-learning model. Fulfillmentmachine-learning model, as used in this disclosure, is a machinelearning model that utilizes alimentary instruction sets as an input andoutputs self-fulfillment instruction sets. Fulfillment machine-learningmodel includes any of the machine-learning models as described herein.For instance and without limitation, fulfillment machine-learning modelmay include a supervised machine-learning model or an unsupervisedmachine-learning model. In yet another non-limiting example, fulfillmentmachine-learning model may include a classification algorithm, includingany of the classification algorithms as described above. Fulfillmentmodule 152 generates a self-fulfillment instruction set utilizing usertraining data and fulfillment machine-learning model.

With continued reference to FIG. 1, fulfillment module 152 is configuredto receive from a user client device an element of user data describinga user geolocation. A user geolocation, as used in this disclosure, isan identification of a real-world geographical location of a user. Auser geolocation may be obtained from a radar source, user client devicesuch as a mobile phone, and/or internet connected device location. Auser geolocation may include a global positioning system (GPS) of asystem. A user geolocation may include geographic coordinates thatspecify the latitude and longitude where a user is located. A usergeolocation may identify a location where a user spends a certain amountof time, such as a house where user resides during the week or anapartment where user stays on the weekend. In yet another non-limitingexample, a user geolocation may identify an office building where a userworks during the day or a home address in a tropical location where auser spends winter months. Fulfillment module 152 generates aself-fulfillment instruction set to identify a self-fulfillment actionlocated within a user geolocation. For instance and without limitation,self-fulfillment instruction set may identify available groceries forsale at a grocery store located within the user geolocation. In yetanother non-limiting example, self-fulfillment instruction set mayrecommend a specific meal that can be purchased at a restaurant locatedwithin the user geolocation. In yet another non-limiting example,self-fulfillment instruction set may recommend a meal delivery optionthat will deliver to the user geolocation. Fulfillment module 152 may beconfigured to receive one or more network transmissions from one or morecomputing devices and/or servers operated by a third party. A thirdparty, may include any party that may offer fulfillment options. Forexample, a third party may include a restaurant that sells meals or agrocery store that sells packaged goods. In yet another non-limitingexample, a third party may include a home kitchen where meals areprepared and available for purchase by a chef. In yet anothernon-limiting example, a third party may include any online or retaillocation where groceries are available for purchase.

With continued reference to FIG. 1, fulfillment module 146 is configuredto receive a user entry containing a completed alimentaryself-fulfillment action. In an embodiment, fulfillment module 152 mayreceive a user entry containing a completed alimentary self-fulfillmentaction from a user client device 156 operated by a user. User clientdevice may include any of the user client devices as described in moredetail below. Fulfillment module 152 may receive a user entry containinga completed alimentary self-fulfillment action utilizing any networkmethodology as described herein. A completed alimentary self-fulfillmentaction as used herein, includes any user entry containing datadescribing as to how a user self-fulfilled. Self-fulfilled as usedherein, includes any action or step a user performed or didn't performbased on an alimentary instruction set. User entry may include a usergenerated response that may include text, graphics, photographs,descriptions, sentences, words, selections, choices, and the likedescribing how a user self-fulfilled an alimentary instruction set 144.For example, user entry may contain a photograph a meal a user consumedfor breakfast the previous day. In yet another non-limiting example,alimentary instruction set 144 may contain a recommendation for a userto increase intake of monounsaturated and polyunsaturated fats toincrease low levels of high density lipoprotein (HDL). User entry mayinclude a user generated response that may contain a description of ameal user consumed for lunch consisting of a chopped salad topped withavocado, walnuts, and chicken. User entry may include a description ofany self-fulfillment action that a user contemplated or thought aboutperforming. For instance and without limitation, user entry may describea list of groceries a user contemplated purchasing or a meal at arestaurant that a user is considering purchasing. User entry may includea graphic such as a photograph a user may take of user's meal andtransmit to fulfillment module. In an embodiment, user may select from alist certain foods user may have consumed. In an embodiment, user maygenerate a user entry at timed intervals, such as after every meal userconsumes or at the end of each day. In an embodiment, user may generatea user entry sporadically or at untimed intervals. For example, a userwho has an alimentary instruction set 144 that includes recommendationsto increase consumption of melatonin rich foods such as cherries whenuser experiences insomnia, may cause a user to generate a user entrysporadically when a user experiences insomnia. Fulfillment module 152may be configured to match user entry containing an alimentaryinstruction set as a function of the user entry to at least aself-fulfillment instruction set as described in more detail below.Fulfillment module 152 may be configured to match user entry containingan alimentary instruction set as a function off the user entry to atleast an alimentary instruction set as described in more detail below.

With continued reference to FIG. 1, fulfillment module 146 is configuredto receive at an image capture device located on computing device awireless transmission from a user client device. An image capturedevice, as used in this disclosure, includes any device suitable to takea picture and/or photograph. Image capture device may include forexample, a camera, mobile phone camera, scanner or the like. In anembodiment, image capture device may be located on user client device,such as a mobile phone camera. Image capture device is configured toreceive a photograph of a completed alimentary self-fulfillment action.For instance and without limitation, image capture device may receive aphotograph of a completed alimentary self-fulfillment action thatcontains receipt data. Receipt data, as used in this disclosure,includes any data describing any purchase of goods relating to aself-fulfillment action. For instance and without limitation, receiptdata may contain data describing groceries a user purchased at a grocerystore. In yet another non-limiting example, receipt data may containdata describing a meal kit that a user purchased or a selection of mealsthat a user ordered from a restaurant and had delivered to the user'soffice. Receipt data may include receipts from online purchases such asgroceries ordered online from a grocery store. Receipt data may includereceipts from purchases in person at a store.

With continued reference to FIG. 1, fulfillment module is configured toclassify a user entry containing a photograph of a self-fulfillmentaction utilizing a self-fulfillment classifier. A classifier, as used inthis disclosure, is a machine-learning model, such as a mathematicalmodel, neural net, or program generated by a machine-learning algorithmknown as a classification algorithm, that sorts inputs into categoriesor bins of data, outputting the categories or bins of data and/or labelsassociated therewith. A self-fulfillment classifier, as used in thisdisclosure, is a classifier configured to input a photograph of aself-fulfillment action and output a self-fulfillment activity categorylabel. Classification may be performed using, without limitation, linearclassifiers such as without limitation logistic regression and/or naiveBayes classifiers, nearest neighbor classifiers such as k-nearestneighbors classifiers, support vector machines, least squares supportvector machines, fisher's linear discriminant, quadratic classifiers,decision trees, boosted trees, random forest classifiers, learningvector quantization, and/or neural network-based classifiers. Aself-fulfillment activity category label, as used in this disclosure, isdata describing a class of a completed alimentary self-fulfillmentaction. A class of a completed alimentary self-fulfillment action mayindicate the method or way in which a self-fulfillment action wascompleted. A class of a completed alimentary self-fulfillment action mayindicate an activity that a user performed For instance and withoutlimitation, a class may indicate if a user consumed food through fooddelivery, if a user went grocery shopping in person at a store, if auser ordered groceries from an online retail app, if a user ordered ameal kit, if a user prepared a meal kit, if a user cooked dinner athome, if a user had a chef prepare a meal, if a user had precooked mealssent to the user's house, if a user bought a carry out meal at a grocerystore and the like. Fulfillment module 152 utilizes a self-fulfillmentactivity category label to update self-fulfillment instruction set. Forinstance and without limitation, fulfillment module 152 may utilize aself-fulfillment category label to recommend subsequent self-fulfillmentactions based on repeated activities and habits of a user. For example,a user entry that contains a photograph of meals a user has ordered fromrestaurants in the area may be utilized by fulfillment module togenerate an updated self-fulfillment instruction set that recommendsother meal options within the user's geographic location. In yet anothernon-limiting example, a user entry that contains a photograph ofgroceries that a user purchased at a grocery store may be utilized toupdate a self-fulfillment instruction that contains instructions for howa user can self-fulfill with subsequent grocery purchases. In yetanother non-limiting example, a user entry that is not classified to aself-fulfillment activity category label may be utilized by fulfillmentmodule 152 to generate a self-fulfillment instruction set that does notcontain other non-selected self-fulfillment category labels insubsequent self-fulfillment instruction sets. For example, threephotographs of user entries classified to a self-fulfillment categorylabel of grocery shopping may be utilized to update a self-fulfillmentinstruction set that contains a self-fulfillment action of groceryshopping and does not contain recommendations for meal delivery or mealsfrom restaurants. With continued referenced to FIG. 1, fulfillmentmodule 152 is configured to generate a loss function. Loss functionincludes any of the loss functions as described above in more detail.Fulfillment module 152 is configured to receive a user input from a userclient device. A user input as used in this disclosure, includes anypreference related to fulfillment. Fulfillment module 152 receives auser input utilizing any network methodology as described herein. A userinput may contain a user preference relating to one or more variablespreferences relating to self-fulfillment. A variable preference, as usedin this disclosure, is data describing a user's selection and/or inputrelating to self-fulfillment variables. Variables may include any factorthat may affect self-fulfillment including cost, access to certainfoods, time to travel to locate certain foods, and the like. Forexample, a variable may specify how far by distance or how far in time auser prefers to travel to a store to purchase groceries. In yet anothernon-limiting example, a variable may specify a user's preferenceregarding product quality, such as if a user prefers to consume organicproduce or conventionally raise meats. In yet another non-limitingexample, a variable may specify if a user prefers to purchase groceriesin person at a particular grocery store or if a user purchases produceat a farm stand or through a community sponsored agriculture (CSA)program. In yet another non-limiting example, a variable may specifycertain foods that a user enjoys eating such as kale, broccoli, andspinach but that a user dislikes red bell pepper. In yet anothernon-limiting example, a variable may specify if a user follows a certaindiet or way of eating based on certain health conditions, ethicalbeliefs, religious reasons and the like. In yet another non-limitingexample, a variable may specify a certain cost or threshold amount thata user is willing to spend on a self-fulfillment action. Fulfillmentmodule 152 generates a loss function utilizing a user input andminimizes the loss function. This may be performed utilizing any of themethodologies as described above in more detail.

With continued reference to FIG. 1, fulfillment module is configured toupdate a self-fulfillment instruction set utilizing an indication ofuser digestibility. Fulfillment module is configured to record a secondbiological extraction. A second biological extraction includes anybiological extraction as described above in more detail. A secondbiological extraction contains an element of user physiological datacontaining an indication as to user digestibility. User digestibilityincludes any process in which nutrition is broken up physically andchemically and converted into a substance suitable for absorption andassimilation into the body. An element of user physiological datacontaining an indication as to user digestibility includes anybiological extraction pertaining to and/or relating to userdigestibility. For example, an element of user physiological datacontaining an indication as to user digestibility may include abiological extraction related to one or more dimensions of the humanbody. An element of user physiological data may contain a stool testanalyzed for parasites or a marker of gut wall absorption analyzed ford-lactate, lactulose, and mannitol levels. In yet another non-limitingexample, an element of user physiological data may contain a blood testanalyzed for one or more nutrient levels. Fulfillment module updates aself-fulfillment instruction set utilizing an indication of userdigestibility. For example, an indication of user digestibility thatindicates a user has an altered microbiome may be utilized to update aself-fulfillment instruction set to contain a recommendation to purchasefoods rich in beneficial bacterial at a grocery store such as sauerkrautor kombucha or to purchase a meal from a restaurant that is high inprebiotic fiber including asparagus, banana, leeks, and onion.

With continued reference to FIG. 1, system 100 may include aclient-interface module 152. Client-interface module 152 may include anysuitable hardware or software module. Client-interface module 152 maydesigned and configured to transmit comprehensive instruction set 140 toat least a user client device 156 associated with the user. A userclient device 156 may include, without limitation, a display incommunication with diagnostic engine 104; display may include anydisplay as described herein. A user client device 156 may include anaddition computing device, such as a mobile device, laptop, desktopcomputer, or the like; as a non-limiting example, the user client device156 may be a computer and/or workstation operated by a medicalprofessional. Output may be displayed on at least a user client device156 using an output graphical user interface; output graphical userinterface may display at least a current prognostic descriptor, at leasta future prognostic descriptor, and/or at least an ameliorative processdescriptor.

With continued reference to FIG. 1, system 100 may include at least anadvisory module executing on the computing device 102. At least anadvisory module 158 may include any suitable hardware or softwaremodule. In an embodiment, at least an advisory module 158 is designedand configured to generate at least an advisory output as a function ofthe comprehensive instruction set 140 and may transmit the advisoryoutput to at least an advisor client device 160. At least an advisorclient device 160 may include any device suitable for use as a userclient device 156 as described above. At least an advisor client device160 may operate on system 100 and may be a user client device 156 asdescribed above; that is, at least an advisory output may be output tothe user client device 156. Alternatively or additionally, at least anadvisor client device 160 may be operated by an informed advisor,defined for the purposes of this disclosure as any person besides theuser who has access to information usable to aid user in interactionwith artificial intelligence advisory system. An informed advisor mayinclude, without limitation, a medical professional such as a doctor,nurse, nurse practitioner, functional medicine practitioner, anyprofessional with a career in medicine, nutrition, genetics, fitness,life sciences, insurance, and/or any other applicable industry that maycontribute information and data to system 100 regarding medical needs.An informed advisor may include a spiritual or philosophical advisor,such as a religious leader, pastor, imam, rabbi, or the like. Aninformed advisor may include a physical fitness advisor, such as withoutlimitation a personal trainer, instructor in yoga or martial arts,sports coach, or the like. Advisory module 158 may generate at least anadvisory output while consulting information contained within advisorydatabase as described below in more detail in reference to FIGS. 15-17.

Referring now to FIG. 2, data incorporated in first training set 106and/or second training set 116 may be incorporated in one or moredatabases. As a non-limiting example, one or elements of physiologicalstate data may be stored in and/or retrieved from a biologicalextraction database 200. A biological extraction database 200 mayinclude any data structure for ordered storage and retrieval of data,which may be implemented as a hardware or software module. A biologicalextraction database 200 may be implemented, without limitation, as arelational database, a key-value retrieval datastore such as a NOSQLdatabase, or any other format or structure for use as a datastore that aperson skilled in the art would recognize as suitable upon review of theentirety of this disclosure. A biological extraction database 200 mayinclude a plurality of data entries and/or records corresponding toelements of physiological data as described above. Data entries and/orrecords may describe, without limitation, data concerning particularphysiological samples that have been collected; entries may describereasons for collection of samples, such as without limitation one ormore conditions being tested for, which may be listed with relatedprognostic labels. Data entries may include prognostic labels and/orother descriptive entries describing results of evaluation of pastphysiological samples, including diagnoses that were associated withsuch samples, prognoses and/or conclusions regarding likelihood offuture diagnoses that were associated with such samples, and/or othermedical or diagnostic conclusions that were derived. Such conclusionsmay have been generated by system 100 in previous iterations of methods,with or without validation of correctness by medical professionals. Dataentries in a biological extraction database 200 may be flagged with orlinked to one or more additional elements of information, which may bereflected in data entry cells and/or in linked tables such as tablesrelated by one or more indices in a relational database; one or moreadditional elements of information may include data associating aphysiological sample and/or a person from whom a physiological samplewas extracted or received with one or more cohorts, includingdemographic groupings such as ethnicity, sex, age, income, geographicalregion, or the like, one or more common diagnoses or physiologicalattributes shared with other persons having physiological samplesreflected in other data entries, or the like. Additional elements ofinformation may include one or more categories of physiological data asdescribed above. Additional elements of information may includedescriptions of particular methods used to obtain physiological samples,such as without limitation physical extraction of blood samples or thelike, capture of data with one or more sensors, and/or any otherinformation concerning provenance and/or history of data acquisition.Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various ways in which data entries in abiological extraction database 200 may reflect categories, cohorts,and/or populations of data consistently with this disclosure.

With continued reference to FIG. 2, diagnostic engine 104 may beconfigured to have a feedback mechanism. In an embodiment, diagnosticengine 104 may be configured to receive a first training set 200 and/ora second training set 220 generated by system 100. For example, dataabout a user that has been previously been analyzed by diagnostic engine104 may be utilized in algorithms by first model 240 and/or second model248. Such algorithms may be continuously updated as a function of suchdata. In yet another embodiment, data analyzed by language processingmodule 216 may be utilized as part of training data generatingalgorithms by first model 240 and/or second model 248 and/or any othermachine learning process performed by diagnostic engine 104.

Referring now to FIG. 3, one or more database tables in biologicalextraction database 200 may include, as a non-limiting example, aprognostic link table 300. Prognostic link table 300 may be a tablerelating physiological sample data as described above to prognosticlabels; for instance, where an expert has entered data relating aprognostic label to a category of physiological sample data and/or to anelement of physiological sample data via first graphical user interface112 as described above, one or more rows recording such an entry may beinserted in prognostic link table 300. Alternatively or additionally,linking of prognostic labels to physiological sample data may beperformed entirely in a prognostic label database as described below.

With continued reference to FIG. 3, biological extraction database 200may include tables listing one or more samples according to samplesource. For instance, and without limitation, biological extractiondatabase 200 may include a fluid sample table 304 listing samplesacquired from a person by extraction of fluids, such as withoutlimitation blood, lymph cerebrospinal fluid, or the like. As anothernon-limiting example, biological extraction database 200 may include asensor data table 308, which may list samples acquired using one or moresensors, for instance as described in further detail below. As a furthernon-limiting example, biological extraction database 200 may include agenetic sample table 312, which may list partial or entire sequences ofgenetic material. Genetic material may be extracted and amplified, as anon-limiting example, using polymerase chain reactions (PCR) or thelike. As a further example, also non-limiting, biological extractiondatabase 200 may include a medical report table 316, which may listtextual descriptions of medical tests, including without limitationradiological tests or tests of strength and/or dexterity or the like.Data in medical report table may be sorted and/or categorized using alanguage processing module 312, for instance, translating a textualdescription into a numerical value and a label corresponding to acategory of physiological data; this may be performed using any languageprocessing algorithm or algorithms as referred to in this disclosure. Asanother non-limiting example, biological extraction database 200 mayinclude a tissue sample table 320, which may record physiologicalsamples obtained using tissue samples. Tables presented above arepresented for exemplary purposes only; persons skilled in the art willbe aware of various ways in which data may be organized in biologicalextraction database 200 consistently with this disclosure.

Referring again to FIG. 2, diagnostic engine 104 and/or another devicein system 100 may populate one or more fields in biological extractiondatabase 200 using expert information, which may be extracted orretrieved from an expert knowledge database 204. An expert knowledgedatabase 204 may include any data structure and/or data store suitablefor use as a biological extraction database 200 as described above.Expert knowledge database 204 may include data entries reflecting one ormore expert submissions of data such as may have been submittedaccording to any process described above in reference to FIG. 1,including without limitation by using first graphical user interface 112and/or second graphical user interface 140. Expert knowledge databasemay include one or more fields generated by language processing module114, such as without limitation fields extracted from one or moredocuments as described above. For instance, and without limitation, oneor more categories of physiological data and/or related prognosticlabels and/or categories of prognostic labels associated with an elementof physiological state data as described above may be stored ingeneralized from in an expert knowledge database 204 and linked to,entered in, or associated with entries in a biological extractiondatabase 200. Documents may be stored and/or retrieved by diagnosticengine 104 and/or language processing module 114 in and/or from adocument database 208; document database 208 may include any datastructure and/or data store suitable for use as biological extractiondatabase 200 as described above. Documents in document database 208 maybe linked to and/or retrieved using document identifiers such as URIand/or URL data, citation data, or the like; persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variousways in which documents may be indexed and retrieved according tocitation, subject matter, author, date, or the like as consistent withthis disclosure.

Referring now to FIG. 4, an exemplary embodiment of an expert knowledgedatabase 204 is illustrated. Expert knowledge database 204 may, as anon-limiting example, organize data stored in the expert knowledgedatabase 204 according to one or more database tables. One or moredatabase tables may be linked to one another by, for instance, commoncolumn values. For instance, a common column between two tables ofexpert knowledge database 200 may include an identifier of an expertsubmission, such as a form entry, textual submission, expert paper, orthe like, for instance as defined below; as a result, a query may beable to retrieve all rows from any table pertaining to a givensubmission or set thereof. Other columns may include any other categoryusable for organization or subdivision of expert data, including typesof expert data, names and/or identifiers of experts submitting the data,times of submission, or the like; persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which expert data from one or more tables may be linked and/orrelated to expert data in one or more other tables.

Still referring to FIG. 4, one or more database tables in expertknowledge database 204 may include, as a non-limiting example, an expertprognostic table 400. Expert prognostic table 400 may be a tablerelating physiological sample data as described above to prognosticlabels; for instance, where an expert has entered data relating aprognostic label to a category of physiological sample data and/or to anelement of physiological sample data via first graphical user interface112 as described above, one or more rows recording such an entry may beinserted in expert prognostic table 400. In an embodiment, a formsprocessing module 404 may sort data entered in a submission via firstgraphical user interface 112 by, for instance, sorting data from entriesin the first graphical user interface 112 to related categories of data;for instance, data entered in an entry relating in the first graphicaluser interface 112 to a prognostic label may be sorted into variablesand/or data structures for storage of prognostic labels, while dataentered in an entry relating to a category of physiological data and/oran element thereof may be sorted into variables and/or data structuresfor the storage of, respectively, categories of physiological data orelements of physiological data. Where data is chosen by an expert frompre-selected entries such as drop-down lists, data may be storeddirectly; where data is entered in textual form, language processingmodule 114 may be used to map data to an appropriate existing label, forinstance using a vector similarity test or other synonym-sensitivelanguage processing test to map physiological data to an existing label.Alternatively or additionally, when a language processing algorithm,such as vector similarity comparison, indicates that an entry is not asynonym of an existing label, language processing module may indicatethat entry should be treated as relating to a new label; this may bedetermined by, e.g., comparison to a threshold number of cosinesimilarity and/or other geometric measures of vector similarity of theentered text to a nearest existent label, and determination that adegree of similarity falls below the threshold number and/or a degree ofdissimilarity falls above the threshold number. Data from expert textualsubmissions 408, such as accomplished by filling out a paper or PDF formand/or submitting narrative information, may likewise be processed usinglanguage processing module 114. Data may be extracted from expert papers412, which may include without limitation publications in medical and/orscientific journals, by language processing module 114 via any suitableprocess as described herein. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various additionalmethods whereby novel terms may be separated from already-classifiedterms and/or synonyms therefore, as consistent with this disclosure.Expert prognostic table 400 may include a single table and/or aplurality of tables; plurality of tables may include tables forparticular categories of prognostic labels such as a current diagnosistable, a future prognosis table, a genetic tendency table, a metabolictendency table, and/or an endocrinal tendency table (not shown), to namea few non-limiting examples presented for illustrative purposes only.

With continued reference to FIG. 4, one or more database tables inexpert knowledge database 204 may include, as a further non-limitingexample tables listing one or more ameliorative process labels; expertdata populating such tables may be provided, without limitation, usingany process described above, including entry of data from secondgraphical user interface 140 via forms processing module 404 and/orlanguage processing module 114, processing of textual submissions 408,or processing of expert papers 412. For instance, and withoutlimitation, an ameliorative nutrition table 416 may list one or moreameliorative processes based on nutritional instructions, and/or linksof such one or more ameliorative processes to prognostic labels, asprovided by experts according to any method of processing and/orentering expert data as described above. As a further example anameliorative action table 420 may list one or more ameliorativeprocesses based on instructions for actions a user should take,including without limitation exercise, meditation, and/or cessation ofharmful eating, substance abuse, or other habits, and/or links of suchone or more ameliorative processes to prognostic labels, as provided byexperts according to any method of processing and/or entering expertdata as described above. As an additional example, an ameliorativesupplement table 424 may list one or more ameliorative processes basedon nutritional supplements, such as vitamin pills or the like, and/orlinks of such one or more ameliorative processes to prognostic labels,as provided by experts according to any method of processing and/orentering expert data as described above. As a further non-limitingexample, an ameliorative medication table 428 may list one or moreameliorative processes based on medications, including withoutlimitation over-the-counter and prescription pharmaceutical drugs,and/or links of such one or more ameliorative processes to prognosticlabels, as provided by experts according to any method of processingand/or entering expert data as described above. As an additionalexample, a counterindication table 432 may list one or morecounter-indications for one or more ameliorative processes;counterindications may include, without limitation allergies to one ormore foods, medications, and/or supplements, side-effects of one or moremedications and/or supplements, interactions between medications, foods,and/or supplements, exercises that should not be used given one or moremedical conditions, injuries, disabilities, and/or demographiccategories, or the like. Tables presented above are presented forexemplary purposes only; persons skilled in the art will be aware ofvarious ways in which data may be organized in expert knowledge database204 consistently with this disclosure.

Referring again to FIG. 2, a prognostic label database 212, which may beimplemented in any manner suitable for implementation of biologicalextraction database 200, may be used to store prognostic labels used insystem 100, including any prognostic labels correlated with elements ofphysiological data in first training set 106 as described above;prognostic labels may be linked to or refer to entries in biologicalextraction database 200 to which prognostic labels correspond. Linkingmay be performed by reference to historical data concerningphysiological samples, such as diagnoses, prognoses, and/or othermedical conclusions derived from physiological samples in the past;alternatively or additionally, a relationship between a prognostic labeland a data entry in biological extraction database 200 may be determinedby reference to a record in an expert knowledge database 204 linking agiven prognostic label to a given category of physiological sample asdescribed above. Entries in prognostic label database 212 may beassociated with one or more categories of prognostic labels as describedabove, for instance using data stored in and/or extracted from an expertknowledge database 204.

Referring now to FIG. 5, an exemplary embodiment of a prognostic labeldatabase 212 is illustrated. Prognostic label database 212 may, as anon-limiting example, organize data stored in the prognostic labeldatabase 212 according to one or more database tables. One or moredatabase tables may be linked to one another by, for instance, commoncolumn values. For instance, a common column between two tables ofprognostic label database 212 may include an identifier of an expertsubmission, such as a form entry, textual submission, expert paper, orthe like, for instance as defined below; as a result, a query may beable to retrieve all rows from any table pertaining to a givensubmission or set thereof. Other columns may include any other categoryusable for organization or subdivision of expert data, including typesof expert data, names and/or identifiers of experts submitting the data,times of submission, or the like; persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which expert data from one or more tables may be linked and/orrelated to expert data in one or more other tables.

Still referring to FIG. 5, one or more database tables in prognosticlabel database 212 may include, as a non-limiting example, a sample datatable 500. Sample data table 500 may be a table listing sample data,along with, for instance, one or more linking columns to link such datato other information stored in prognostic label database 212. In anembodiment, sample data 504 may be acquired, for instance frombiological extraction database 200, in a raw or unsorted form, and maybe translated into standard forms, such as standard units ofmeasurement, labels associated with particular physiological datavalues, or the like; this may be accomplished using a datastandardization module 508, which may perform unit conversions. Datastandardization module 508 may alternatively or additionally map textualinformation, such as labels describing values tested for or the like,using language processing module 114 or equivalent components and/oralgorithms thereto.

Continuing to refer to FIG. 5, prognostic label database 212 may includea sample label table 512; sample label table 512 may list prognosticlabels received with and/or extracted from physiological samples, forinstance as received in the form of sample text 516. A languageprocessing module 114 may compare textual information so received toprognostic labels and/or form new prognostic labels according to anysuitable process as described above. Sample prognostic link table maycombine samples with prognostic labels, as acquired from sample labeltable and/or expert knowledge database 204; combination may be performedby listing together in rows or by relating indices or common columns oftwo or more tables to each other. Tables presented above are presentedfor exemplary purposes only; persons skilled in the art will be aware ofvarious ways in which data may be organized in expert knowledge database204 consistently with this disclosure.

Referring again to FIG. 2, first training set 106 may be populated byretrieval of one or more records from biological extraction database 200and/or prognostic label database 212; in an embodiment, entriesretrieved from biological extraction database 200 and/or prognosticlabel database 212 may be filtered and or select via query to match oneor more additional elements of information as described above, so as toretrieve a first training set 106 including data belonging to a givencohort, demographic population, or other set, so as to generate outputsas described below that are tailored to a person or persons with regardto whom system 100 classifies physiological samples to prognostic labelsas set forth in further detail below. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which records may be retrieved from biological extraction database200 and/or prognostic label database to generate a first training set toreflect individualized group data pertaining to a person of interest inoperation of system and/or method, including without limitation a personwith regard to whom at least a physiological sample is being evaluatedas described in further detail below. Diagnostic engine 104 mayalternatively or additionally receive a first training set 106 and storeone or more entries in biological extraction database 200 and/orprognostic label database 212 as extracted from elements of firsttraining set 106.

Still referring to FIG. 2, system 100 may include or communicate with anameliorative process label database 216; an ameliorative process labeldatabase 216 may include any data structure and/or datastore suitablefor use as a biological extraction database 200 as described above. Anameliorative process label database 216 may include one or more entrieslisting labels associated with one or more ameliorative processes asdescribed above, including any ameliorative labels correlated withprognostic labels in second training set 116 as described above;ameliorative process labels may be linked to or refer to entries inprognostic label database 212 to which ameliorative process labelscorrespond. Linking may be performed by reference to historical dataconcerning prognostic labels, such as therapies, treatments, and/orlifestyle or dietary choices chosen to alleviate conditions associatedwith prognostic labels in the past; alternatively or additionally, arelationship between an ameliorative process label and a data entry inprognostic label database 212 may be determined by reference to a recordin an expert knowledge database 204 linking a given ameliorative processlabel to a given category of prognostic label as described above.Entries in ameliorative process label database 212 may be associatedwith one or more categories of prognostic labels as described above, forinstance using data stored in and/or extracted from an expert knowledgedatabase 204.

Referring now to FIG. 6, an exemplary embodiment of an ameliorativeprocess label database 216 is illustrated. Ameliorative process labeldatabase 216 may, as a non-limiting example, organize data stored in theameliorative process label database 216 according to one or moredatabase tables. One or more database tables may be linked to oneanother by, for instance, common column values. For instance, a commoncolumn between two tables of ameliorative process label database 216 mayinclude an identifier of an expert submission, such as a form entry,textual submission, expert paper, or the like, for instance as definedbelow; as a result, a query may be able to retrieve all rows from anytable pertaining to a given submission or set thereof. Other columns mayinclude any other category usable for organization or subdivision ofexpert data, including types of expert data, names and/or identifiers ofexperts submitting the data, times of submission, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various ways in which expert data from one or more tablesmay be linked and/or related to expert data in one or more other tables.

Still referring to FIG. 6, ameliorative process label database 216 mayinclude a prognostic link table 600; prognostic link table may linkameliorative process data to prognostic label data, using any suitablemethod for linking data in two or more tables as described above.Ameliorative process label database 216 may include an ameliorativenutrition table 604, which may list one or more ameliorative processesbased on nutritional instructions, and/or links of such one or moreameliorative processes to prognostic labels, for instance as provided byexperts according to any method of processing and/or entering expertdata as described above, and/or using one or more machine-learningprocesses as set forth in further detail below. As a further example anameliorative action table 608 may list one or more ameliorativeprocesses based on instructions for actions a user should take,including without limitation exercise, meditation, and/or cessation ofharmful eating, substance abuse, or other habits, and/or links of suchone or more ameliorative processes to prognostic labels, as provided byexperts according to any method of processing and/or entering expertdata as described above and/or using one or more machine-learningprocesses as set forth in further detail below. As an additionalexample, an ameliorative supplement table 612 may list one or moreameliorative processes based on nutritional supplements, such as vitaminpills or the like, and/or links of such one or more ameliorativeprocesses to prognostic labels, as provided by experts according to anymethod of processing and/or entering expert data as described aboveand/or using one or more machine-learning processes as set forth infurther detail below. As a further non-limiting example, an ameliorativemedication table 616 may list one or more ameliorative processes basedon medications, including without limitation over-the-counter andprescription pharmaceutical drugs, and/or links of such one or moreameliorative processes to prognostic labels, as provided by expertsaccording to any method of processing and/or entering expert data asdescribed above and/or using one or more machine-learning processes asset forth in further detail below. As an additional example, acounterindication table 620 may list one or more counter-indications forone or more ameliorative processes; counterindications may include,without limitation allergies to one or more foods, medications, and/orsupplements, side-effects of one or more medications and/or supplements,interactions between medications, foods, and/or supplements, exercisesthat should not be used given one or more medical conditions, injuries,disabilities, and/or demographic categories, or the like; this may beacquired using expert submission as described above and/or using one ormore machine-learning processes as set forth in further detail below.Tables presented above are presented for exemplary purposes only;persons skilled in the art will be aware of various ways in which datamay be organized in ameliorative process database 216 consistently withthis disclosure

Referring again to FIG. 2, second training set 116 may be populated byretrieval of one or more records from prognostic label database 212and/or ameliorative process label database 216; in an embodiment,entries retrieved from prognostic label database 212 and/or ameliorativeprocess label database 216 may be filtered and or select via query tomatch one or more additional elements of information as described above,so as to retrieve a second training set 116 including data belonging toa given cohort, demographic population, or other set, so as to generateoutputs as described below that are tailored to a person or persons withregard to whom system 100 classifies prognostic labels to ameliorativeprocess labels as set forth in further detail below. Persons skilled inthe art, upon reviewing the entirety of this disclosure, will be awareof various ways in which records may be retrieved from prognostic labeldatabase 212 and/or ameliorative process label database 216 to generatea second training set 116 to reflect individualized group datapertaining to a person of interest in operation of system and/or method,including without limitation a person with regard to whom at least aphysiological sample is being evaluated as described in further detailbelow. Diagnostic engine 104 may alternatively or additionally receive asecond training set 116 and store one or more entries in prognosticlabel database 212 and/or ameliorative process label database 216 asextracted from elements of second training set 116.

With continued reference to FIG. 2, diagnostic engine 104 may receive anupdate to one or more elements of data represented in first training set106 and/or second training set 116, and may perform one or moremodifications to first training set 106 and/or second training set 116,or to biological extraction database 200, expert knowledge database 204,prognostic label database 212, and/or ameliorative process labeldatabase 216 as a result. For instance, a physiological sample may turnout to have been erroneously recorded; diagnostic engine 104 may removeit from first training set 106, second training set 116, biologicalextraction database 200, expert knowledge database 204, prognostic labeldatabase 212, and/or ameliorative process label database 216 as aresult. As a further example, a medical and/or academic paper, or astudy on which it was based, may be revoked; diagnostic engine 104 mayremove it from first training set 106, second training set 116,biological extraction database 200, expert knowledge database 204,prognostic label database 212, and/or ameliorative process labeldatabase 216 as a result. Information provided by an expert may likewisebe removed if the expert loses credentials or is revealed to have actedfraudulently.

Continuing to refer to FIG. 2, elements of data first training set 106,second training set 116, biological extraction database 200, expertknowledge database 204, prognostic label database 212, and/orameliorative process label database 216 may have temporal attributes,such as timestamps; diagnostic engine 104 may order such elementsaccording to recency, select only elements more recently entered forfirst training set 106 and/or second training set 116, or otherwise biastraining sets, database entries, and/or machine-learning models asdescribed in further detail below toward more recent or less recententries. Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various ways in which temporal attributesof data entries may be used to affect results of methods and/or systemsas described herein.

Referring now to FIG. 7, machine-learning algorithms used by prognosticlabel learner 126 may include supervised machine-learning algorithms,which may, as a non-limiting example be executed using a supervisedlearning module 700 executing on diagnostic engine 104 and/or on anothercomputing device in communication with diagnostic engine 104, which mayinclude any hardware or software module. Supervised machine learningalgorithms, as defined herein, include algorithms that receive atraining set relating a number of inputs to a number of outputs, andseek to find one or more mathematical relations relating inputs tooutputs, where each of the one or more mathematical relations is optimalaccording to some criterion specified to the algorithm using somescoring function. For instance, a supervised learning algorithm may useelements of physiological data as inputs, prognostic labels as outputs,and a scoring function representing a desired form of relationship to bedetected between elements of physiological data and prognostic labels;scoring function may, for instance, seek to maximize the probabilitythat a given element of physiological state data 108 and/or combinationof elements of physiological data is associated with a given prognosticlabel and/or combination of prognostic labels to minimize theprobability that a given element of physiological state data 108 and/orcombination of elements of physiological state data 108 is notassociated with a given prognostic label and/or combination ofprognostic labels. Scoring function may be expressed as a risk functionrepresenting an “expected loss” of an algorithm relating inputs tooutputs, where loss is computed as an error function representing adegree to which a prediction generated by the relation is incorrect whencompared to a given input-output pair provided in first training set106. Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various possible variations of supervisedmachine learning algorithms that may be used to determine relationbetween elements of physiological data and prognostic labels. In anembodiment, one or more supervised machine-learning algorithms may berestricted to a particular domain for instance, a supervisedmachine-learning process may be performed with respect to a given set ofparameters and/or categories of parameters that have been suspected tobe related to a given set of prognostic labels, and/or are specified aslinked to a medical specialty and/or field of medicine covering aparticular set of prognostic labels. As a non-limiting example, aparticular set of blood test biomarkers and/or sensor data may betypically used by cardiologists to diagnose or predict variouscardiovascular conditions, and a supervised machine-learning process maybe performed to relate those blood test biomarkers and/or sensor data tothe various cardiovascular conditions; in an embodiment, domainrestrictions of supervised machine-learning procedures may improveaccuracy of resulting models by ignoring artifacts in training data.Domain restrictions may be suggested by experts and/or deduced fromknown purposes for particular evaluations and/or known tests used toevaluate prognostic labels. Additional supervised learning processes maybe performed without domain restrictions to detect, for instance,previously unknown and/or unsuspected relationships betweenphysiological data and prognostic labels.

Referring now to FIG. 8, ameliorative process label learner 130 may beconfigured to perform one or more supervised learning processes, asdescribed above; supervised learning processes may be performed by asupervised learning module 800 executing on diagnostic engine 104 and/oron another computing device in communication with diagnostic engine 104,which may include any hardware or software module. For instance, asupervised learning algorithm may use prognostic labels as inputs,ameliorative labels as outputs, and a scoring function representing adesired form of relationship to be detected between prognostic labelsand ameliorative labels; scoring function may, for instance, seek tomaximize the probability that a given prognostic label and/orcombination of prognostic labels is associated with a given ameliorativelabel and/or combination of ameliorative labels to minimize theprobability that a given prognostic label and/or combination ofprognostic labels is not associated with a given ameliorative labeland/or combination of ameliorative labels. In an embodiment, one or moresupervised machine-learning algorithms may be restricted to a particulardomain; for instance, a supervised machine-learning process may beperformed with respect to a given set of parameters and/or categories ofprognostic labels that have been suspected to be related to a given setof ameliorative labels, for instance because the ameliorative processescorresponding to the set of ameliorative labels are hypothesized orsuspected to have an ameliorative effect on conditions represented bythe prognostic labels, and/or are specified as linked to a medicalspecialty and/or field of medicine covering a particular set ofprognostic labels and/or ameliorative labels. As a non-limiting example,a particular set prognostic labels corresponding to a set ofcardiovascular conditions may be typically treated by cardiologists, anda supervised machine-learning process may be performed to relate thoseprognostic labels to ameliorative labels associated with varioustreatment options, medications, and/or lifestyle changes.

With continued reference to FIG. 8, ameliorative process label learner130 may perform one or more unsupervised machine-learning processes asdescribed above; unsupervised processes may be performed by anunsupervised learning module 804 executing on diagnostic engine 104and/or on another computing device in communication with diagnosticengine 104, which may include any hardware or software module. Forinstance, and without limitation, ameliorative process label learner 130and/or diagnostic engine 104 may perform an unsupervised machinelearning process on second training set 116, which may cluster data ofsecond training set 116 according to detected relationships betweenelements of the second training set 116, including without limitationcorrelations of prognostic labels to each other and correlations ofameliorative labels to each other; such relations may then be combinedwith supervised machine learning results to add new criteria forameliorative process label learner 130 to apply in relating prognosticlabels to ameliorative labels. As a non-limiting, illustrative example,an unsupervised process may determine that a first prognostic label 110correlates closely with a second prognostic label 118, where the firstprognostic label 110 has been linked via supervised learning processesto a given ameliorative label, but the second has not; for instance, thesecond prognostic label 118 may not have been defined as an input forthe supervised learning process, or may pertain to a domain outside of adomain limitation for the supervised learning process. Continuing theexample, a close correlation between first prognostic label 110 andsecond prognostic label 118 may indicate that the second prognosticlabel 118 is also a good match for the ameliorative label; secondprognostic label 118 may be included in a new supervised process toderive a relationship or may be used as a synonym or proxy for the firstprognostic label 110 by ameliorative process label learner 130.Unsupervised processes performed by ameliorative process label learner130 may be subjected to any domain limitations suitable for unsupervisedprocesses performed by prognostic label learner 126 as described above.

Still referring to FIG. 8, diagnostic engine 104 and/or ameliorativeprocess label learner 130 may detect further significant categories ofprognostic labels, relationships of such categories to ameliorativelabels, and/or categories of ameliorative labels using machine-learningprocesses, including without limitation unsupervised machine-learningprocesses as described above; such newly identified categories, as wellas categories entered by experts in free-form fields as described above,may be added to pre-populated lists of categories, lists used toidentify language elements for language learning module, and/or listsused to identify and/or score categories detected in documents, asdescribed above. In an embodiment, as additional data is added to system100, ameliorative process label learner 130 and/or diagnostic engine 104may continuously or iteratively perform unsupervised machine-learningprocesses to detect relationships between different elements of theadded and/or overall data; in an embodiment, this may enable system 100to use detected relationships to discover new correlations between knownbiomarkers, prognostic labels, and/or ameliorative labels and one ormore elements of data in large bodies of data, such as genomic,proteomic, and/or microbiome-related data, enabling future supervisedlearning and/or lazy learning processes to identify relationshipsbetween, e.g., particular clusters of genetic alleles and particularprognostic labels and/or suitable ameliorative labels. Use ofunsupervised learning may greatly enhance the accuracy and detail withwhich system may detect prognostic labels and/or ameliorative labels.

With continued reference to FIG. 8, ameliorative labels may be generatedbased on classification of the at least a prognostic output.Classification as used herein includes pairing or grouping prognosticoutputs as a function of some shared commonality. Prognostic outputs maybe grouped with certain endocrine disorders such as diabetes, metabolicsyndrome, and/or pre-diabetes which may generate an ameliorative labelassociated with a physical exercise recommendation that may includeaerobic exercises such as running, brisk walking, cycling, and/orswimming in an attempt to reduce elevated blood sugar levels in patientswith such endocrine disorders. Prognostic outputs grouped with certainalarm conditions such as chest pains, shortness of breath, cold sweat,and sudden dizziness may generate an ameliorative label associated withmedical tests, diagnostics, and/or procedures for a suspected myocardialinfarction such as an electrocardiogram (EKG), measurement of serumtroponin levels, complete blood count (CBC), chest x-ray,echocardiogram, cardiac CT, cardiac MRI, and/or coronarycatheterization. Ameliorative label may be generated based on groupingssuch as severity of prognostic output. For example, a user who presentswith mild chest pain and some indigestion may be grouped to a categoryof prognostic labels that is serious but not alarming and may generatean ameliorative label that includes a blood test for troponin levels torule out a potential myocardial infarction. A user who presents withcrushing chest pain, tingling down one or both arms, shortness ofbreath, and cold and clammy skin may be grouped into a category of alarmso as to generate an ameliorative label that includes a cardiac CT orcardiac MRI to see if user is suffering from some type of coronaryocclusion and may be a candidate for a possible coronarycatheterization. In yet another non-limiting example, ameliorative labelmay be generated as a function of severity and/or progression ofprognostic output. For example, a prognostic label that includes adiagnosis of hypothyroidism as evidenced by a thyroid stimulating level(TSH) of 6.0 (normal range is 1.4-5.5) may generate an ameliorativelabel that includes 150 mcg per day of iodine supplementation to lowerTSH within normal limits due to mild TSH elevation and/or mildprogression of hypothyroidism. A prognostic output that includes adiagnosis of hypothyroidism as evidenced by a TSH of 15.0 may generatean ameliorative label that includes 300 mcg per day of iodinesupplementation as well as a prescription for a T-4 containingmedication such as Synthroid and a T-3 containing medication such asCytomel due to the more severe progression of hypothyroidism.Classification of at least a prognostic output may include staging of aprognostic label. Staging may include dividing a disease state orcondition into categories on a spectrum of disease progression andsymptomology. For example, a user with a prognostic output thatindicates peri-menopause as evidenced by increasing prevalence of hotflashes may generate an ameliorative label that includes arecommendation for supplementation with black cohosh, while a user witha prognostic output that indicates progression to menopause as evidencedby persistent hot flashes, night sweats, absence of menstruation, dryhair, and fatigue may generate an ameliorative label that containsrecommendations for supplementation with bio-identical hormonereplacement therapy such as estrone (E1), estradiol (E2), estriol (E3),progesterone, testosterone, dehydroepiandrosterone (DHEA), and/orpregnenolone. In yet another non-limiting example, early stage of adisease such as Alzheimer's disease as demonstrated by mild cognitiveimpairment may generate an ameliorative label that includes norecommended medical treatment except for watchful waiting. However,advanced Alzheimer's disease may warrant an ameliorative label thatincludes medical intervention and may require a prescription medication.Ameliorative label may be generated by any of the methodologies asdescribed below in reference to FIG. 19.

Continuing to view FIG. 8, ameliorative process label learner 130 may beconfigured to perform a lazy learning process as a function of thesecond training set 116 and the at least a prognostic output to producethe at least an ameliorative output; a lazy learning process may includeany lazy learning process as described above regarding prognostic labellearner 126. Lazy learning processes may be performed by a lazy learningmodule 808 executing on diagnostic engine 104 and/or on anothercomputing device in communication with diagnostic engine 104, which mayinclude any hardware or software module. Ameliorative output 812 may beprovided to a user output device as described in further detail below.

In an embodiment, and still referring to FIG. 8, ameliorative processlabel learner 130 may generate a plurality of ameliorative labels havingdifferent implications for a particular person. For instance, where aprognostic label indicates that a person has a magnesium deficiency,various dietary choices may be generated as ameliorative labelsassociated with correcting the deficiency, such as ameliorative labelsassociated with consumption of almonds, spinach, and/or dark chocolate,as well as ameliorative labels associated with consumption of magnesiumsupplements. In such a situation, ameliorative process label learner 130and/or diagnostic engine 104 may perform additional processes to resolveambiguity. Processes may include presenting multiple possible results toa medical practitioner, informing the medical practitioner of variousoptions that may be available, and/or that follow-up tests, procedures,or counseling may be required to select an appropriate choice.Alternatively or additionally, processes may include additional machinelearning steps. For instance, ameliorative process label learner 130 mayperform one or more lazy learning processes using a more comprehensiveset of user data to identify a more probably correct result of themultiple results. Results may be presented and/or retained withrankings, for instance to advise a medical professional of the relativeprobabilities of various ameliorative labels being correct or idealchoices for a given person; alternatively or additionally, ameliorativelabels associated with a probability of success or suitability below agiven threshold and/or ameliorative labels contradicting results of theadditional process, may be eliminated. As a non-limiting example, anadditional process may reveal that a person is allergic to tree nuts,and consumption of almonds may be eliminated as an ameliorative label tobe presented.

Continuing to refer to FIG. 8, ameliorative process label learner 130may be designed and configured to generate further training data and/orto generate outputs using longitudinal data 816. As used herein,longitudinal data 816 may include a temporally ordered series of dataconcerning the same person, or the same cohort of persons; for instance,longitudinal data 816 may describe a series of blood samples taken oneday or one month apart over the course of a year. Longitudinal data 816may related to a series of samples tracking response of one or moreelements of physiological data recorded regarding a person undergoingone or more ameliorative processes linked to one or more ameliorativeprocess labels. Ameliorative process label learner 130 may track one ormore elements of physiological data and fit, for instance, a linear,polynomial, and/or splined function to data points; linear, polynomial,or other regression across larger sets of longitudinal data, using, forinstance, any regression process as described above, may be used todetermine a best-fit graph or function for the effect of a givenameliorative process over time on a physiological parameter. Functionsmay be compared to each other to rank ameliorative processes; forinstance, an ameliorative process associated with a steeper slope incurve representing improvement in a physiological data element, and/or ashallower slope in a curve representing a slower decline, may be rankedhigher than an ameliorative process associated with a less steep slopefor an improvement curve or a steeper slope for a curve marking adecline. Ameliorative processes associated with a curve and/or terminaldata point representing a value that does not associate with apreviously detected prognostic label may be ranked higher than one thatis not so associated. Information obtained by analysis of longitudinaldata 816 may be added to ameliorative process database and/or secondtraining set.

Referring now to FIG. 9, an exemplary embodiment of alimentaryinstruction label learner 134 is illustrated. alimentary instructionlabel learner 134 may be configured to perform one or more supervisedlearning processes, as described above; supervised learning processesmay be performed by a supervised learning module 904 executing ondiagnostic engine 104 and/or on another computing device incommunication with diagnostic engine 104, which may include any hardwareor software module. For instance, a supervised learning algorithm mayuse prognostic labels as inputs, alimentary labels as outputs, and ascoring function representing a desired form of relationship to bedetected between prognostic labels and alimentary labels; scoringfunction may, for instance, seek to maximize the probability that agiven prognostic label and/or combination of prognostic labels isassociated with a given alimentary label and/or combination ofalimentary labels to minimize the probability that a given prognosticlabel and/or combination of prognostic labels is not associated with agiven alimentary label and/or combination of alimentary labels. In anembodiment, one or more supervised machine-learning algorithms may berestricted to a particular domain; for instance, a supervisedmachine-learning process may be performed with respect to a given set ofparameters and/or categories of prognostic labels that have beensuspected to be related to a given set of alimentary labels, forinstance because the alimentary processes corresponding to the set ofalimentary labels are hypothesized or suspected to have an ameliorativeeffect on conditions represented by the prognostic labels, and/or arespecified as linked to a medical specialty and/or field of medicinecovering a particular set of prognostic labels and/or alimentary labels.As a non-limiting example, a particular set prognostic labelscorresponding to a set of cardiovascular conditions may be typicallytreated by cardiologists, and a supervised machine-learning process maybe performed to relate those prognostic labels to alimentary labelsassociated with various alimentary options.

With continued reference to FIG. 9, alimentary instruction label learner134 may perform one or more unsupervised machine-learning processes asdescribed above; unsupervised processes may be performed by anunsupervised learning module 908 executing on diagnostic engine 104and/or on another computing device in communication with diagnosticengine 104, which may include any hardware or software module. Forinstance, and without limitation, alimentary instruction label learner134 and/or diagnostic engine 104 may perform an unsupervised machinelearning process on second training set 116, which may cluster data ofsecond training set 116 according to detected relationships betweenelements of the second training set 116, including without limitationcorrelations of prognostic labels to each other and correlations ofalimentary labels to each other; such relations may then be combinedwith supervised machine learning results to add new criteria foralimentary instruction label learner 134 to apply in relating prognosticlabels to alimentary labels. As a non-limiting, illustrative example, anunsupervised process may determine that a first prognostic label 110correlates closely with a second prognostic label 118, where the firstprognostic label 110 has been linked via supervised learning processesto a given alimentary label, but the second has not; for instance, thesecond prognostic label 118 may not have been defined as an input forthe supervised learning process, or may pertain to a domain outside of adomain limitation for the supervised learning process. Continuing theexample, a close correlation between first prognostic label 110 andsecond prognostic label 118 may indicate that the second prognosticlabel 118 is also a good match for the alimentary label; secondprognostic label 118 may be included in a new supervised process toderive a relationship or may be used as a synonym or proxy for the firstprognostic label 110 by alimentary instruction label learner 134.Unsupervised processes performed by alimentary instruction label learner134 may be subjected to any domain limitations suitable for unsupervisedprocesses performed by prognostic label learner 126 as described above.

Still referring to FIG. 9, diagnostic engine 104 and/or alimentaryinstruction label learner 134 may detect further significant categoriesof prognostic labels, relationships of such categories to alimentarylabels, and/or categories of alimentary labels using machine-learningprocesses, including without limitation unsupervised machine-learningprocesses as described above; such newly identified categories, as wellas categories entered by experts in free-form fields as described above,may be added to pre-populated lists of categories, lists used toidentify language elements for language learning module, and/or listsused to identify and/or score categories detected in documents, asdescribed above. In an embodiment, as additional data is added todiagnostic engine 104, alimentary instruction label learner 134 and/ordiagnostic engine 104 may continuously or iteratively performunsupervised machine-learning processes to detect relationships betweendifferent elements of the added and/or overall data; in an embodiment,this may enable diagnostic engine 104 to use detected relationships todiscover new correlations between known biomarkers, prognostic labels,and/or alimentary labels and one or more elements of data in largebodies of data, such as genomic, proteomic, and/or microbiome-relateddata, enabling future supervised learning and/or lazy learning processesto identify relationships between, e.g., particular clusters of geneticalleles and particular prognostic labels and/or suitable alimentarylabels. Use of unsupervised learning may greatly enhance the accuracyand detail with which system may detect prognostic labels and/oralimentary labels.

Continuing to view FIG. 9, alimentary instruction label learner 134 maybe configured to perform a lazy learning process as a function of thesecond training set 116 and the at least a prognostic output to producethe at least an alimentary output; a lazy learning process may includeany lazy learning process as described above regarding prognostic labellearner 126. Lazy learning processes may be performed by a lazy learningmodule 912 executing on diagnostic engine 104 and/or on anothercomputing device in communication with diagnostic engine 104, which mayinclude any hardware or software module. Alimentary output 916 may beprovided to a user output device as described in further detail below.

With continued reference to FIG. 9, alimentary instruction label learner134 may generate a plurality of alimentary labels having differentimplications for a particular person. For instance, where a prognosticlabel indicates that a person has a magnesium deficiency, variousdietary choices may be generated as alimentary labels associated withcorrecting the deficiency, such as alimentary labels associated withconsumption of almonds, spinach, and/or dark chocolate, as well asalimentary labels associated with consumption of magnesium supplements.In such a situation, alimentary instruction label learner 134 and/ordiagnostic engine 104 may perform additional processes to resolveambiguity. Processes may include presenting multiple possible results toa medical practitioner, informing the medical practitioner of variousoptions that may be available, and/or that follow-up tests, procedures,or counseling may be required to select an appropriate choice.Alternatively or additionally, processes may include additional machinelearning steps. For instance, alimentary instruction label learner 134may perform one or more lazy learning processes using a morecomprehensive set of user data to identify a more probably correctresult of the multiple results. Results may be presented and/or retainedwith rankings, for instance to advise a medical professional of therelative probabilities of various alimentary labels being correct orideal choices for a given person; alternatively or additionally,alimentary labels associated with a probability of success orsuitability below a given threshold and/or alimentary labelscontradicting results of the additional process, may be eliminated. As anon-limiting example, an additional process may reveal that a person isallergic to tree nuts, and consumption of almonds may be eliminated asan alimentary label to be presented.

Continuing to refer to FIG. 9, alimentary instruction label learner 134may be designed and configured to generate further training data and/orto generate outputs using longitudinal data 920. As used herein,longitudinal data 920 may include a temporally ordered series of dataconcerning the same person, or the same cohort of persons; for instance,longitudinal data 920 may describe a series of blood samples taken oneday or one month apart over the course of a year. Longitudinal data 920may relate to a series of samples tracking response of one or moreelements of physiological data recorded regarding a person undergoingone or more alimentary processes linked to one or more alimentaryprocess labels. Alimentary instruction label learner 134 may track oneor more elements of physiological data and fit, for instance, a linear,polynomial, and/or splined function to data points; linear, polynomial,or other regression across larger sets of longitudinal data, using, forinstance, any regression process as described above, may be used todetermine a best-fit graph or function for the effect of a givenalimentary process over time on a physiological parameter. Functions maybe compared to each other to rank alimentary processes; for instance, analimentary process associated with a steeper slope in curve representingimprovement in a physiological data element, and/or a shallower slope ina curve representing a slower decline, may be ranked higher than analimentary process associated with a less steep slope for an improvementcurve or a steeper slope for a curve marking a decline. Alimentaryprocesses associated with a curve and/or terminal data pointrepresenting a value that does not associate with a previously detectedprognostic label may be ranked higher than one that is not soassociated. Information obtained by analysis of longitudinal data 920may be added to alimentary process database and/or second training set.

With continued reference to FIG. 9, embodiments of diagnostic engine 104may furnish augmented intelligence systems that facilitate diagnostic,prognostic, curative, and/or therapeutic decisions by nutrition, diet,and wellness professionals such as nutritionists, dietitians, orapplicable trainers/coaches/mentors. Diagnostic engine 104 may providefully automated tools and resources for each applicable professional tohandle, process, diagnosis, develop alimentary, diet, or wellness plans,facilitate and monitor all patient implementation, and record eachpatient status. Provision of expert system elements via expert inputsand document-driven language analysis may ensure that recommendationsgenerated by diagnostic engine 104 are backed by the very best medicaland alimentary knowledge and practices in the world. Models and/orlearners with access to data in depth may enable generation ofrecommendations that are directly personalized for each patient,providing complete confidence, mitigated risk, and completetransparency. Access to well-organized and personalized knowledge indepth may greatly enhance efficiency of nutrition consultations; inembodiments, a comprehensive session may be completed in as little as 10minutes. Recommendations may further suggest follow up testing, therapy,and/or delivery of substances, ensuring an effective ongoing treatmentand prognostic plan.

Referring now to FIG. 10, an exemplary embodiment of a plan generatormodule 138 is illustrated. Comprehensive instruction set 140 includes atleast a current prognostic descriptor 1000 which as used in thisdisclosure is an element of data describing a current prognostic statusbased on at least one prognostic output. Plan generator module 138 mayproduce at least a current prognostic descriptor 1000 using at least aprognostic output. In an embodiment, plan generator module 138 mayinclude a label synthesizer 1004. Label synthesizer 1004 may include anysuitable software or hardware module. In an embodiment, labelsynthesizer 1004 may be designed and configured to combine a pluralityof labels in at least a prognostic output together to provide maximallyefficient data presentation. Combination of labels together may includeelimination of duplicate information. For instance, label synthesizer1004 and/or a computing device 102 may be designed and configure todetermine a first prognostic label of the at least a prognostic label isa duplicate of a second prognostic label of the at least a prognosticlabel and eliminate the first prognostic label. Determination that afirst prognostic label is a duplicate of a second prognostic label mayinclude determining that the first prognostic label is identical to thesecond prognostic label; for instance, a prognostic label generated fromtest data presented in one biological extraction of at least abiological extraction may be the same as a prognostic label generatedfrom test data presented in a second biological extraction of at least abiological extraction. As a further non-limiting example, a firstprognostic label may be synonymous with a second prognostic label, wheredetection of synonymous labels may be performed, without limitation, bya language processing module 114 as described above.

Continuing to refer to FIG. 10, label synthesizer 1004 may groupprognostic labels according to one or more classification systemsrelating the prognostic labels to each other. For instance, plangenerator module 138 and/or label synthesizer 1004 may be configured todetermine that a first prognostic label of the at least a prognosticlabel and a second prognostic label of the at least a prognostic labelbelong to a shared category. A shared category may be a category ofconditions or tendencies toward a future condition to which each offirst prognostic label and second prognostic label belongs; as anexample, lactose intolerance and gluten sensitivity may each be examplesof digestive sensitivity, for instance, which may in turn share acategory with food sensitivities, food allergies, digestive disorderssuch as celiac disease and diverticulitis, or the like. Shared categoryand/or categories may be associated with prognostic labels as well. Agiven prognostic label may belong to a plurality of overlappingcategories. Plan generator module 138 may be configured to add acategory label associated with a shared category to comprehensiveinstruction set 140, where addition of the label may include addition ofthe label and/or a datum linked to the label, such as a textual ornarrative description. In an embodiment, relationships betweenprognostic labels and categories may be retrieved from a prognosticlabel classification database 1008, for instance by generating a queryusing one or more prognostic labels of at least a prognostic output,entering the query, and receiving one or more categories matching thequery from the prognostic label classification database 1008.

Referring now to FIG. 11, an exemplary embodiment of a prognostic labelclassification database 1008 is illustrated. Prognostic labelclassification database 1008 may be implemented as any database and/ordatastore suitable for use as biological extraction database 200 asdescribed above. One or more database tables in prognostic labelclassification database 1008 may include, without limitation, asymptomatic classification table 1200; symptomatic classification table1200 may relate each prognostic label to one or more categories ofsymptoms associated with that prognostic label. As a non-limitingexample, symptomatic classification table 1200 may include recordsindicating that each of lactose intolerance and gluten sensitivityresults in symptoms including gas buildup, bloating, and abdominal pain.One or more database tables in prognostic label classification database1108 may include, without limitation, a systemic classification table1204; systemic classification table 1204 may relate each prognosticlabel to one or more systems associated with that prognostic label. As anon-limiting example, systemic classification table 1204 may includerecords indicating each of lactose intolerance and gluten sensitivityaffects the digestive system; two digestive sensitivities linked toallergic or other immune responses may additionally be linked insystemic classification table 1204 to the immune system. One or moredatabase tables in prognostic label classification database 1008 mayinclude, without limitation, a body part classification table 1008; bodypart classification table 1208 may relate each prognostic label to oneor more body parts associated with that prognostic label. As anon-limiting example, body part classification table 1208 may includerecords indicating each of psoriasis and rosacea affects the skin of aperson. One or more database tables in prognostic label classificationdatabase 1008 may include, without limitation, a causal classificationtable 1212; causal classification table 1212 may relate each prognosticlabel to one or more causes associated with that prognostic label. As anon-limiting example, causal classification table 1212 may includerecords indicating each of type 2 diabetes and hypertension may haveobesity as a cause. The above-described tables, and entries therein, areprovided solely for exemplary purposes. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variousadditional examples for tables and/or relationships that may be includedor recorded in prognostic classification table consistently with thisdisclosure.

Referring again to FIG. 10, plan generator module 138 may be configuredto generate current prognostic descriptor 1000 by converting one or moreprognostic labels into narrative language. As a non-limiting example,plan generator module 138 may include a narrative language unit 1012,which may be configured to determine an element of narrative languageassociated with at least a prognostic label and include the element ofnarrative language in current prognostic label descriptor. Narrativelanguage unit 1012 may implement this, without limitation, by using alanguage processing module 114 to detect one or more associationsbetween prognostic labels, or lists of prognostic labels, and phrasesand/or statements of narrative language. Alternatively or additionally,narrative language unit 1012 may retrieve one or more elements ofnarrative language from a narrative language database 1016, which maycontain one or more tables associating prognostic labels and/or groupsof prognostic labels with words, sentences, and/or phrases of narrativelanguage. One or more elements of narrative language may be included incomprehensive instruction set 140, for instance for display to a user astext describing a current prognostic status of the user. Currentprognostic descriptor 1000 may further include one or more images; oneor more images may be retrieved by plan generator module 138 from animage database 1020, which may contain one or more tables associatingprognostic labels, groups of prognostic labels, current prognosticdescriptors 1000, or the like with one or more images.

With continued reference to FIG. 10, comprehensive instruction set 140may include one or more follow-up suggestions, which may include,without limitation, suggestions for acquisition of an additionalbiological extraction; in an embodiment, additional biologicalextraction may be provided to diagnostic engine 104, which may triggerrepetition of one or more processes as described above, includingwithout limitation generation of prognostic output, refinement orelimination of ambiguous prognostic labels of prognostic output,generation of ameliorative output, and/or refinement or elimination ofambiguous ameliorative labels of ameliorative output. For instance,where a pegboard test result suggests possible diagnoses of Parkinson'sdisease, Huntington's disease, ALS, and MS as described above, follow-upsuggestions may include suggestions to perform endocrinal tests, genetictests, and/or electromyographic tests; results of such tests mayeliminate one or more of the possible diagnoses, such that asubsequently displayed output only lists conditions that have not beeneliminated by the follow-up test. Follow-up tests may include anyreceipt of any biological extraction as described above.

With continued reference to FIG. 10, comprehensive instruction set 140may include one or more elements of contextual information, includingwithout limitation any patient medical history such as current labresults, a current reason for visiting a medical professional, currentstatus of one or more currently implemented treatment plans,biographical information concerning the patient, and the like. One ormore elements of contextual information may include goals a patientwishes to achieve with a medical visit or session, and/or as result ofinteraction with diagnostic engine 104. Contextual information mayinclude one or more questions a patient wishes to have answered in amedical visit and/or session, and/or as a result of interaction withdiagnostic engine 104. Contextual information may include one or morequestions to ask a patient. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various forms ofcontextual information that may be included, consistently with thisdisclosure.

With continued reference to FIG. 10, comprehensive instruction set 140may include at least a future prognostic descriptor 1024. As usedherein, a future prognostic descriptor 1024 is an element of datadescribing a future prognostic status based on at least one prognosticoutput, which may include without limitation a desired furtherprognostic status. In an embodiment, future prognostic descriptor 1024may include any element suitable for inclusion in current prognosticdescriptor 1000. Future prognostic descriptor 1024 may be generatedusing any processes, modules, and/or components suitable for generationof current prognostic descriptor 1000 as described above.

Still referring to FIG. 10, comprehensive instruction set 140 includesat least an ameliorative process descriptor 1028, which as defined inthis disclosure an element of data describing one or more ameliorativeprocesses to be followed based on at least one ameliorative output; atleast an ameliorative process descriptor 1028 may include descriptorsfor ameliorative processes usable to achieve future prognosticdescriptor 1024. Plan generator module 138 may produce at least anameliorative process descriptor 1028 using at least a prognostic output.In an embodiment, label synthesizer 1004 may be designed and configuredto combine a plurality of labels in at least an ameliorative outputtogether to provide maximally efficient data presentation. Combinationof labels together may include elimination of duplicate information. Forinstance, label synthesizer 1004 and/or a computing device 102 may bedesigned and configure to determine a first ameliorative label of the atleast an ameliorative label is a duplicate of a second ameliorativelabel of the at least an ameliorative label and eliminate the firstameliorative label. Determination that a first ameliorative label is aduplicate of a second ameliorative label may include determining thatthe first ameliorative label is identical to the second ameliorativelabel; for instance, a ameliorative label generated from test datapresented in one biological extraction of at least a biologicalextraction may be the same as a ameliorative label generated from testdata presented in a second biological extraction of at least abiological extraction. As a further non-limiting example, a firstameliorative label may be synonymous with a second ameliorative label,where detection of synonymous labels may be performed, withoutlimitation, by a language processing module 114 as described above.

Continuing to refer to FIG. 10, label synthesizer 1004 may groupameliorative labels according to one or more classification systemsrelating the ameliorative labels to each other. For instance, plangenerator module 138 and/or label synthesizer 1004 may be configured todetermine that a first ameliorative label of the at least anameliorative label and a second ameliorative label of the at least anameliorative label belong to a shared category. A shared category may bea category of conditions or tendencies toward a future condition towhich each of first ameliorative label and second ameliorative labelbelongs; as an example, lactose intolerance and gluten sensitivity mayeach be examples of digestive sensitivity, for instance, which may inturn share a category with food sensitivities, food allergies, digestivedisorders such as celiac disease and diverticulitis, or the like. Sharedcategory and/or categories may be associated with ameliorative labels aswell. A given ameliorative label may belong to a plurality ofoverlapping categories. Plan generator module 138 may be configured toadd a category label associated with a shared category to comprehensiveinstruction set 140, where addition of the label may include addition ofthe label and/or a datum linked to the label, such as a textual ornarrative description. In an embodiment, relationships betweenameliorative labels and categories may be retrieved from an ameliorativelabel classification database 1032, for instance by generating a queryusing one or more ameliorative labels of at least an ameliorativeoutput, entering the query, and receiving one or more categoriesmatching the query from the ameliorative label classification database1032.

Referring now to FIG. 12, an exemplary embodiment of an ameliorativelabel classification database 1032 is illustrated. Ameliorative labelclassification database 1032 may be implemented as any database and/ordatastore suitable for use as biological extraction database 200 asdescribed above. One or more database tables in ameliorative labelclassification database 1032 may include, without limitation, anintervention category table 1200; intervention 1200 may relate eachameliorative label to one or more categories associated with thatameliorative label. As a non-limiting example, intervention categorytable 1200 may include records indicating that each of a plan to consumea given quantity of almonds and a plan to consume less meat maps to acategory of nutritional instruction, while a plan to jog for 30 minutesper day maps to a category of activity. One or more database tables inameliorative label classification database 1032 may include, withoutlimitation, a nutrition category table 1204; nutrition category table1204 may relate each ameliorative label pertaining to nutrition to oneor more categories associated with that ameliorative label. As anon-limiting example, nutrition category table 1204 may include recordsindicating that each of a plan to consume more almonds and a plan toconsume more walnuts qualifies as a plan to consume more nuts, as wellas a plan to consume more protein. One or more database tables inameliorative label classification database 1032 may include, withoutlimitation, an action category table 1208; action category table 1208may relate each ameliorative label pertaining to an action to one ormore categories associated with that ameliorative label. As anon-limiting example, action category table 1208 may include recordsindicating that each of a plan jog for 30 minutes a day and a plan toperform a certain number of sit-ups per day qualifies as an exerciseplan. One or more database tables in ameliorative label classificationdatabase 1032 may include, without limitation, a medication categorytable 1212; medication category table 1212 may relate each ameliorativelabel associated with a medication to one or more categories associatedwith that ameliorative label. As a non-limiting example, medicationcategory table 1212 may include records indicating that each of a planto take an antihistamine and a plan to take an anti-inflammatory steroidbelongs to a category of allergy medications. One or more databasetables in ameliorative label classification database 1032 may include,without limitation, a supplement category table 1216; supplementcategory table 1216 may relate each ameliorative label pertaining to asupplement to one or more categories associated with that ameliorativelabel. As a non-limiting example, supplement category table 1216 mayinclude records indicating that each of a plan to consume a calciumsupplement and a plan to consume a vitamin D supplement corresponds to acategory of supplements to aid in bone density. Ameliorative labels maybe mapped to each of nutrition category table 1204, action categorytable 1208, supplement category table 1216, and medication categorytable 1212 using intervention category table 1200. The above-describedtables, and entries therein, are provided solely for exemplary purposes.Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various additional examples for tablesand/or relationships that may be included or recorded in ameliorativeclassification table consistently with this disclosure.

Referring again to FIG. 10, plan generator module 138 may be configuredto generate ameliorative process descriptor 1028 by converting one ormore ameliorative labels into narrative language. As a non-limitingexample, plan generator module 138 may include a narrative language unit1012, which may be configured to determine an element of narrativelanguage associated with at least an ameliorative label and include theelement of narrative language in current ameliorative label descriptor.Narrative language unit 1012 may implement this, without limitation, byusing a language processing module 114 to detect one or moreassociations between ameliorative labels, or lists of ameliorativelabels, and phrases and/or statements of narrative language.Alternatively or additionally, narrative language unit 1012 may retrieveone or more elements of narrative language from narrative languagedatabase 1016, which may contain one or more tables associatingameliorative labels and/or groups of ameliorative labels with words,sentences, and/or phrases of narrative language. One or more elements ofnarrative language may be included in comprehensive instruction set 140,for instance for display to a user as text describing a currentameliorative status of the user. Ameliorative process descriptor 1028may further include one or more images; one or more images may beretrieved by plan generator module 138 from an image database 1020,which may contain one or more tables associating ameliorative labels,groups of ameliorative labels, ameliorative process descriptors 1028, orthe like with one or more images.

Referring now to FIG. 13, and exemplary embodiment of a narrativelanguage database 1016 is illustrated. Narrative language database 1016may be implemented as any database and/or datastore suitable for use asbiological extraction database 200 as described above. One or moredatabase tables in narrative language database 1016 may include, withoutlimitation, a prognostic description table 1300, which may linkprognostic labels to narrative descriptions associated with prognosticlabels. One or more database tables in narrative language database 1016may include, without limitation, an ameliorative description table 1304,which may link ameliorative process labels to narrative descriptionsassociated with ameliorative process labels. One or more database tablesin narrative language database 1016 may include, without limitation, acombined description table 1308, which may link combinations ofprognostic labels and ameliorative labels to narrative descriptionsassociated with the combinations. One or more database tables innarrative language database 1016 may include, without limitation, aparagraph template table 1312, which may contain one or more templatesof paragraphs, pages, reports, or the like into which images and text,such as images obtained from image database 1020 and text obtained fromprognostic description table 1300, ameliorative description table 1304,and combined description table 1308 may be inserted. Tables in narrativedescription table 1016 may be populated, as a non-limiting example,using submissions from experts, which may be collected according to anyprocesses described above. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various way sin whichentries in narrative description table 1016 may be categorized and/ororganized.

Referring now to FIG. 14, an exemplary embodiment of an image database1020 is illustrated. Image database 1020 may be implemented as anydatabase and/or datastore suitable for use as biological extractiondatabase 200 as described above. One or more database tables in imagedatabase 102 may include, without limitation, a prognostic image table1400, which may link prognostic labels to images associated withprognostic labels. One or more database tables in image database 1020may include, without limitation, an ameliorative image table 1404, whichmay link ameliorative process labels to images associated withameliorative process labels. One or more database tables in imagedatabase 1020 may include, without limitation, a combined descriptiontable 1408, which may link combinations of prognostic labels andameliorative labels to images associated with the combinations. One ormore database tables in image database 102 may include, withoutlimitation, a prognostic video table 1412, which may link prognosticlabels to videos associated with prognostic labels. One or more databasetables in image database 1020 may include, without limitation, anameliorative video table 1416, which may link ameliorative processlabels to videos associated with ameliorative process labels. One ormore database tables in image database 1020 may include, withoutlimitation, a combined video table 1420, which may link combinations ofprognostic labels and ameliorative labels to videos associated with thecombinations. Tables in image database 1020 may be populated, withoutlimitation, by submissions by experts, which may be provided accordingto any process or process steps described in this disclosure forcollection of expert submissions.

Referring again to FIG. 10, plan generator module 138 may be configuredto receive at least an element of user data and filter diagnostic outputusing the at least an element of user data. At least an element of userdata, as used herein, is any element of data describing the user, userneeds, and/or user preferences. At least an element of user data mayinclude a constitutional restriction. At least a constitutionalrestriction may include any health-based reason that a user may beunable to engage in a given ameliorative process; at least aconstitutional restriction may include any counter-indication asdescribed above, including an injury, a diagnosis of somethingpreventing use of one or more ameliorative processes, an allergy orfood-sensitivity issue, a medication that is counter-indicated, or thelike. At least an element of user data may include at least a userpreference. At least a user preference may include, without limitation,any preference to engage in or eschew any ameliorative process and/orother potential elements of a comprehensive instruction set 140,including religious preferences such as forbidden foods, medicalinterventions, exercise routines, or the like.

Referring to FIG. 15, an exemplary embodiment of a user database 1036 isillustrated. User database 1036 may be implemented as any databaseand/or datastore suitable for use as described above. One or moredatabase tables in user database 1036 may include, without limitation, aconstitution restriction table 1500; at least a constitutionalrestriction may be linked to a given user and/or user identifier in aconstitutional restriction table 1500. One or more database tables inuser database 1036 may include, without limitation, a user preferencetable 1504; at least a user preference may be linked to a given userand/or user identifier in a user preference table 1504.

Referring now to FIG. 16, an exemplary embodiment of alimentaryinstruction set module 142 is illustrated. In one embodiment, thealimentary instruction set generator module 142 may be configured togenerate an alimentary instruction set comprising a plurality ofinformation reflecting a comprehensive list of meals, supplements, andprocesses aimed towards resolving any identified issues, suggestions, ordeficiencies as a function of the comprehensive instruction set 140.Alimentary instruction set generator module 142 may produce at least analimentary instruction set process descriptor 1028 using at least analimentary instruction set output. In an embodiment, alimentaryinstruction set generator module may include a label synthesizer 1004 asdescribed above.

In one embodiment, and still referring to FIG. 16, the alimentaryinstruction set may be transmitted to a user via a graphical userinterface coupled to user client device 156 associated with useroperating in or subscribing to network 100. Alimentary instruction set144 may be utilized to aid a user in performing alimentary instructionset 144 through self-fulfilling to achieve and/or maintain vibrantconstitution. Self-fulfillment may include any food preparation,consuming food through food delivery, arranging for a vitamin/supplementcoaching service, constitutional supplement delivery service, groceryshopping, arranging grocery delivery, picking up take-out from a foodpreparation center, buying a carry away meal at a grocery store orhealth food store, preparing a meal kit, cooking a meal from scratch inone's home, having a chef deliver and/or prepare a meal at a user's homeor work, and the like.

Continuing to refer to FIG. 16, alimentary instruction set generatormodule 142 is designed and configured to an alimentary instruction set144 based on comprehensive instruction set 140. In an embodiment,alimentary instruction set generator module 142 may generate alimentaryinstruction set 144 based on the integration of data associated withcomprehensive instruction set 140, any applicable external sources, andany applicable database within system 100 or physical performance entitynetwork 302. Generation of alimentary instruction set 144 may includeidentification of one or more alimentary instructions in comprehensiveinstruction set, and insertion of the one or more alimentaryinstructions in the alimentary instruction set 144; for instance,alimentary instruction set 144 may be formed, wholly or partially, byaggregating alimentary instructions from comprehensive instruction set140 and combining the aggregated alimentary instructions using narrativelanguage module, narrative language database, image database, or thelike, according to any process suitable for generation of comprehensiveinstruction set as described above.

With continued reference to FIG. 16, alimentary instruction setgenerator module 142 may generate alimentary instruction set 144 basedon alimentary data and non-alimentary data in order to facilitate bothmedicinal and holistic components in alimentary instruction set 144specific to a user. In one embodiment, alimentary data may be identifiedand aggregated into a subset of applicable alimentary data based on atleast a biological extraction and comprehensive instruction set 140. Inapplication, alimentary instruction set 144 may comprise a plurality ofalimentary data specific to user that is able to be used by machinelearning and artificial intelligence systems in order to continuouslyupdate or modify training sets, and ultimately comprehensive instructionset 140 and alimentary instruction set 144 based on updated orprogressions associated with implementation of alimentary instructionset 144 by user. Alimentary data and non-alimentary data may includecompilations of instruction sets received over a period of time, thecompilations may account for improvements or modifications associatedwith user. Alimentary instruction set 144 may further includeinstructions over time, in which the alimentary instructions may changein response to changes in a user's data and/or prognosis. Alternativelyor additionally, system 100 may periodically iterate through one or moreprocesses as described in this disclosure, such that repeatedreevaluations may modify alimentary instruction set 144 as informationconcerning user and/or biological extractions obtained from the userchange over time.

With continued reference to FIG. 16, alimentary instruction setgenerator module 142 may identify a non-alimentary instruction withincomprehensive instruction set 140, determine an alimentary analog to thenon-alimentary instruction and introduce the alimentary analog into thealimentary instruction set and/or use the alimentary analog to updatethe self-fulfillment instruction set. An alimentary analog, as usedherein, is an alimentary process or instruction that achieves a similarpurpose to a non-alimentary process and/or instruction. As anon-limiting example, certain foods such as grapefruit may act to lowerblood sugar; where the impact of consuming a particular quantity of suchfoods is similar to or the same as an impact of consuming a blood sugarmedication, the former may be an alimentary analog of the latter. In oneembodiment, non-alimentary data within comprehensive instruction set 140may be subsequently substituted in alimentary instruction set 144 withalimentary data configured to provide user with holistic solutions toissues that were initially treated with non-holistic approaches. Forexample, if initially diagnostic output indicates that the blood sugarof user is abnormally high then comprehensive instruction set 140 mayrecommend that user take applicable medications classified asnon-alimentary in order to lower the blood sugar immediately. However,alimentary instruction set 144 may subsequently or concurrently provideone or more sets of instructions to remedy the improved blood sugar ofuser via an alimentary solution such as increased consumption ofgrapefruits, configured to be executed by a user. As a further example,a supplement initially presented in comprehensive instruction set 140may be subsequently replaced, in alimentary instruction set 144, by aspecific food categorized as alimentary in order to remedy the issues inwhich the initial supplement sought to address. In another example,alimentary data and alimentary solutions may be incorporated intoalimentary instruction set 144 upon one or more determinations that thealimentary data and implementations of the alimentary solution are moreefficient than non-alimentary solutions initially included in alimentaryinstruction set 144. Alimentary data and alimentary solutions may alsobe substituted for less efficient alimentary solutions. For example, ifuser, based on comprehensive instruction set 140, is deemed to need aboost in HDL, then a secondary alimentary solution of eating certainfoods may be determined more efficient than a primary alimentarysolution of increasing cardio activity.

Still referring to FIG. 16, alimentary instruction set generator module142 may generate alimentary instruction set 144, at least in part, byidentifying at least a negative effect associated with an ameliorativeinstruction of comprehensive instruction set 140; at least a negativeeffect may include a “side-effect” of an ameliorative process, such as aside effect of a medication, an increase risk of a type of injuryassociated with an exercise program, or the like. Alimentary instructionset generator module 142 may determine an alimentary instruction thatalleviates the at least a negative effect; for instance, a side-effectof a medication may be alleviated and/or prevented by consumption of analimentary element tending to alleviate the side-effect. As anon-limiting example, a medication that may cause fluid retention andedema may be provided in comprehensive instruction set 140; alimentaryinstruction set generator module 142 may determine that consumption ofan alimentary element having a diuretic effect, such as a food or drinkcontaining caffeine, may act to prevent or alleviate fluid retention. Asa further non-limiting example, comprehensive instruction set 140 mayinclude an instruction for a user to increase his or her exerciseregimen, or to begin a new regimen of regular exercise; acounterindication and/or other element of data may indicate an elevatedrisk of joint injury and/or inflammation as a result of the increasedexercise, which may be alleviated or prevented by a lower-calorie diet,consumption of foods containing glucosamine or some other ingredientassociated with a reduced risk of joint pain.

Continuing to refer to FIG. 16, alimentary instruction set generatormodule 142 may determine an alimentary instruction that alleviates theat least a negative effect using machine-learning processes and/ormodules as described above; for instance, and without limitation,alimentary instruction set generator module 142 may provide at least anegative effect to ameliorative process label learner and/or alimentaryinstruction set label leaner in the form of at least a prognostic label;ameliorative process label learner and/or alimentary instruction setlabel leaner may generate one or more ameliorative labels associatedwith an alimentary process for alleviating the at least a negativeeffect.

Continuing to refer to FIG. 16, label synthesizer 1004 may groupalimentary labels according to one or more classification systemsrelating the alimentary labels to each other. For instance, plangenerator module 138 and/or label synthesizer 1004 may be configured todetermine that a first alimentary label of the at least an alimentarylabel and a second alimentary label of the at least an alimentary labelbelong to a shared category. A shared category may be a category ofalimentary elements to which each of first alimentary label and secondalimentary label belongs; for instance, a first alimentary labelassociated with tofu and a second alimentary label associated with nutsmay each be grouped as a protein source. A given ameliorative label maybelong to a plurality of overlapping categories. Plan generator module138 may be configured to add a category label associated with a sharedcategory to alimentary instruction set 144, where addition of the labelmay include addition of the label and/or a datum linked to the label,such as a textual or narrative description.

With continued reference to FIG. 16, label synthesizer 1004 may bedesigned and configured to combine a plurality of labels in at least thealimentary instruction set output together to provide maximallyefficient data presentation. Combination of labels together may includeelimination of duplicate information. For instance, label synthesizer1004 and/or a computing device 102 may be designed and configure todetermine a first alimentary instruction set label of the at least analimentary instruction set label is a duplicate of a second alimentaryinstruction set label of the at least a alimentary instruction set labeland eliminate the first alimentary instruction set label. Determinationthat a first alimentary instruction set label is a duplicate of a secondalimentary instruction set label may include determining that the firstalimentary instruction set label is identical to the second alimentaryinstruction set label; for instance, a alimentary instruction set labelgenerated from test data presented in one biological extraction of atleast a biological extraction may be the same as a alimentaryinstruction set label generated from test data presented in a secondbiological extraction of at least a biological extraction. As a furthernon-limiting example, a first alimentary instruction set label may besynonymous with a second alimentary instruction set label, wheredetection of synonymous labels may be performed, without limitation, bya language processing module 114 as described above.

In one embodiment, and still referring to FIG. 16, label synthesizer1004 may group alimentary instruction set labels according to one ormore classification systems relating the alimentary instruction setlabels to each other. For instance, plan generator module 138 and/orlabel synthesizer 1004 may be configured to determine that a firstalimentary instruction set label of the at least an alimentaryinstruction set label and a second alimentary instruction set label ofthe at least a alimentary instruction set label belong to a sharedcategory. A shared category may be a category of conditions ortendencies toward a future condition to which each of first alimentaryinstruction set label and second alimentary instruction set labelbelongs; as an example, lactose intolerance and gluten sensitivity mayeach be examples of digestive sensitivity, for instance, which may inturn share a category with food sensitivities, food allergies, digestivedisorders such as celiac disease and diverticulitis, or the like. Sharedcategory and/or categories may be associated with alimentary instructionset labels as well. A given alimentary instruction set label may belongto a plurality of overlapping categories. Plan generator module 138 maybe configured to add a category label associated with a shared categoryto comprehensive instruction set 140, where addition of the label mayinclude addition of the label and/or a datum linked to the label, suchas a textual or narrative description. In an embodiment, relationshipsbetween alimentary instruction set labels and categories may beretrieved from an alimentary instruction set label classificationdatabase 1600, for instance by generating a query using one or morealimentary instruction set labels of at least a alimentary instructionset output, entering the query, and receiving one or more categoriesmatching the query from the alimentary instruction set labelclassification database 1600. In one embodiment, the alimentaryinstruction set label classification database 1600 is configured togenerate queries based on preferences of user. Preferences may be basedupon religious, dietary (vegan/gluten-free), lifestyle, or any otherapplicable factor associated with user that is able to be manifested inthe alimentary instruction set.

With continued reference to FIG. 16, in one embodiment, alimentaryinstruction set generator module 142 may be configured to generatealimentary instruction set process descriptor 1028 by converting one ormore alimentary instruction set labels into narrative language. As anon-limiting example, plan generator module 168 may include and/orcommunicate with narrative language unit 1012, which may be configuredto determine an element of narrative language associated with at leastan alimentary instruction set label and include the element of narrativelanguage in current alimentary instruction set label descriptor.Narrative language unit 1012 may implement this, without limitation, byusing a language processing module 114 to detect one or moreassociations between alimentary instruction set labels, or lists ofalimentary instruction set labels, and phrases and/or statements ofnarrative language. Alternatively or additionally, narrative languageunit 1012 may retrieve one or more elements of narrative language fromnarrative language database 1016, which may contain one or more tablesassociating alimentary instruction set labels and/or groups ofalimentary instruction set labels with words, sentences, and/or phrasesof narrative language. One or more elements of narrative language may beincluded in alimentary instruction set, for instance for display to auser as text describing a current alimentary instruction set status ofthe user. Alimentary instruction set process descriptor 1028 may furtherinclude one or more images; one or more images may be retrieved by plangenerator module 138 from an image database 1120, which may contain oneor more tables associating alimentary instruction set labels, groups ofalimentary instruction set labels, alimentary instruction set processdescriptors 1028, or the like with one or more images.

With continued reference to FIG. 16, in an embodiment, relationshipsbetween alimentary labels and categories may be retrieved from analimentary instruction label classification database 1600, for instanceby generating a query using one or more alimentary labels of at least analimentary output, entering the query, and receiving one or morecategories matching the query from the alimentary instruction labelclassification database 1600.

Referring now to FIG. 17, an exemplary embodiment of an alimentaryinstruction label classification database 1700 is illustrated.Alimentary instruction label classification database 1700 may operate onthe diagnostic engine 104. Alimentary instruction label classificationdatabase 1700 may be implemented as any database and/or datastoresuitable for use as a database. One or more database tables inalimentary instruction label classification database 1700 may include,without limitation, an intervention category table 1700; an interventionmay relate each alimentary label to one or more categories of conditionsto be addressed by an alimentary instruction associated with thatalimentary label, such as a nutritional imbalance to be corrected or thelike. One or more database tables in alimentary instruction labelclassification database 1700 may include, without limitation, analimentary category table 1704; which may associate an alimentaryinstruction label with one or more categories of nutritional properties,foodstuffs, or the like. One or more database tables in alimentaryinstruction label classification database 1700 may include, withoutlimitation, an action category table 2508, which may describe one ormore categories of actions, such as calorie reduction, sugar intakereduction, or the like, to which a given alimentary instruction maybelong. One or more database tables in alimentary instruction labelclassification database 1700 may include, without limitation, asupplement table 2512, which may describe a supplement that relates to anutritional need filled by an alimentary instruction.

Referring now to FIG. 18, an exemplary embodiment of self-fulfillmentlearner 148 is illustrated. Self-fulfillment learner 148 may beconfigured to perform one or more supervised learning processes, asdescribed above; supervised learning processes may be performed by asupervised learning module 904 executing on diagnostic engine 104 and/oron another computing device in communication with diagnostic engine 104,which may include any hardware or software module. For example,supervised learning algorithm may use alimentary instruction set asinputs, and user entries containing an alimentary self-fulfillmentaction as outputs and/or self-fulfillment instruction set as output anda scoring function representing a desired form of relationship to bedetected between alimentary self-fulfillment action and alimentaryinstruction sets; scoring function may, for instance, seek to maximizethe probability that a given alimentary instruction set is associatedwith an alimentary self-fulfillment action. In yet another non-limitingexample, supervised learning algorithm may use self-fulfillmentinstruction set as inputs and user entries containing an alimentaryself-fulfillment action as output and a scoring function representing adesired form of relationship to be detected between alimentaryself-fulfillment action and self-fulfillment instruction sets; scoringfunction may, for instance seek to maximize the probability that a givenself-fulfillment instruction set is associated with an alimentaryself-fulfillment action. In an embodiment, one or more supervisedmachine-learning algorithms may be restricted to a particular domain;for instance, a supervised machine-learning process may be performedwith respect to a given set of parameters and/or categories ofself-fulfillment instruction sets that have been suspected to be relatedto a given set of user entries containing an alimentary self-fulfillmentaction for instance because the user entries containing an alimentaryself-fulfillment action corresponding to the self-fulfillmentinstruction set are hypothesized or suspected to be linked to a field ofactions or group of actions. For example, a particular set ofself-fulfillment instruction sets relating to obtaining groceries suchas creating grocery lists, ordering groceries, shopping for groceries,and putting groceries away may all relate to obtaining groceries, and asupervised machine-learning process may be performed to relate theseself-fulfillment actions to those contained within a self-fulfillmentinstruction set.

With continued reference to FIG. 18, self-fulfillment learner 148 mayperform one or more supervised machine-learning processes as describedabove, including a loss function analysis utilizing linear regressionbased on past user interactions with system 100, such as informationcollected from user entries and alimentary instruction sets and/orself-fulfillment instruction sets. Loss function analysis may usesupervised machine-learning processes and algorithms to iterate andconverge towards a minimum where further tweaks to the variables producelittle or zero changes in the loss or convergence by optimizing weightsutilized by machine learning algorithms. Self-fulfillment learner 148may utilize variables to model relationships between past interactionsbetween a user such as previously generated user entries andself-fulfillment instruction sets and/or alimentary instruction sets.Loss function analysis may utilize variables that may be weighted andadjusted to predict outcomes. Variables may be personalized based onuser inputs and weighted based on user inputs. For example, a user mayweight one variable as being more important than another while anotheruser may attribute equal weight to each variable. Variables may becontained with a variables database 1804 as described in more detailbelow in reference to FIG. 19. Loss function analysis may utilize pastuser entries 1800 to generate outputs such as self-fulfillmentinstruction set 146. Past user entries 1800 may include any informationpertaining to user's previous interactions with system 100. Past userentries 1800 may include for example, previous user entries containingself-fulfillment actions, previous self-fulfillment instruction setsgenerated for a user, previous prognostic labels, previous ameliorativeprocess labels, and/or previously alimentary instruction sets generatedfor a user.

With continued reference to FIG. 18, self-fulfillment learner 148 mayutilize linear loss function algorithms customized around a user andbased on user entries and past user performances to more accuratelygenerate an alimentary instruction set 144 for a user, aself-fulfillment instruction set 146 for a user, and/or to updateinformation and training sets utilized by plan generator module 138.Loss function algorithms may utilize weighted variables customized to auser. Loss function algorithms may minimize distance between variablesand may seek to minimize distance variable to variable. In anembodiment, after a user has submitted a user entry, the loss functionmay be re-run and updated. For example, if a user found a certainingredient at a grocery store then self-fulfillment instruction set maybe re-generated to update based on this development. Loss functionalgorithms may utilize weighted variables that are customized to a user.For example, user entries that contain trends and patterns as toself-fulfillment actions may be utilized by self-fulfillment learner togenerate self-fulfillment instruction sets based on user trends andpatterns to self-fulfill. For example, a user who enters user entriesthat show a frequency of cooking meals at home may be utilized byself-fulfillment learner 148 to generate self-fulfillment instructionsets that include recipes for the user or suggestions as to potentialnew ingredients to try. In yet another non-limiting example, a user whoenters user entries that show a frequency of eating out at restaurantsmay be utilized by self-fulfillment learner 148 to generateself-fulfillment instruction sets that contain very basic recipes toprepare at home or that contain recommendations as to where a user canbuy a meal on the go in user's area.

Referring now to FIG. 19, an exemplary embodiment of variables database1804 is illustrated. Variables database 1804 may be implemented as anydatabase and/or datastore suitable for use as described above. One ormore database tables in variables database 1804 may include, withoutlimitation, a user habits table 1904; user habits may containinformation pertaining to ways in which a user self-fulfills such as apreference for eating out at restaurants, shopping for groceries,ordering meal kits, cooking at home, having a chef prepare meals, andthe like. One or more database tables in variables database 1804 mayinclude without limitation, a product quality table; product quality maycontain information relating to quality of food that a user typicallyconsumes, such as a preference for organic produce, wild raised seafood,sustainably grown meats, free range poultry, locally sourced productsand/or ingredients, products grown without the use of pesticides and thelike. One or more database tables in variables database 1804 may includewithout limitation, a product ingredients table 1912; productingredients may include information pertaining to if a certain food oritem fulfills an alimentary instruction set. For example, a product suchas kale, milk, and spinach may be categorized as containing calcium toaid a user in consuming more calcium rich foods while ingredients suchas coconut oil and macaroons may be categorized as containing lauricacid. One or more database tables in variables database 1804 may includewithout limitation, cost table 1916; cost may include informationrelating to user cost preference; cost preference may include userpreference for eating out at restaurants versus cooking at home, buyinggroceries at a store versus cost to have groceries delivered, cost fororganic versus inorganic products, cost for buying groceries as comparedto having meals delivered, user budget for nutrition and supplements,and the like. One or more database tables in variables database 1804 mayinclude without limitation, travel time table 1920; travel time mayinclude information relating to how far a user is willing to travel fornutrition such as for example the miles or minutes a user will drive ina car to a restaurant or grocery store. One or more database tables invariables database 1804 may include without limitation, food preferencetable 1924; food preferences may include a user's preference to consumecertain foods or food groups, such as for example a user's preference toconsume chicken and beef but a dislike of plant proteins such as tofuand lentils. One or more database tables in variables database 1804 mayinclude without limitation, product availability table 1928; productavailability may include information as to whether certain products,foods, meals, supplements and the like are available in certaingeographical locations. For example, fish tacos may be available inAnchorage, Alaska but not in Little Rock, Arkansas, as hazelnuts may bebountiful in the Pacific Northwest but scarce in Anchorage, Alaska. Oneor more database tables in variables database 1804 may include withoutlimitation, miscellaneous table 1932; miscellaneous may include othervariables that may be utilized but have not been discussed above.

Referring back now to FIG. 18, self-fulfillment learner 148 may performone or more unsupervised machine-learning processes as described above,unsupervised processes may be performed by an unsupervised learningmodule 908 executing on diagnostic engine 104 and/or on anothercomputing device in communication with diagnostic engine 104, which mayinclude any hardware or software module. For instance and withoutlimitation, self-fulfillment learner 148 may perform an unsupervisedmachine learning process on second training set 116, which may clusterdata of second training set 116 according to detected relationshipsbetween elements of the second training set 116, including for examplerelationships between user entries and alimentary instruction setsand/or self-fulfillment instruction sets; such information may then becombined with supervised machine learning results to add new criteriafor self-fulfillment learning 148 to apply in relating between userentries and alimentary instruction sets and/or self-fulfillmentinstruction sets.

With continued reference to FIG. 18, self-fulfillment learner 148 may beconfigured to perform a lazy learning process as a function of firsttraining set 106 and/or second training set 116 to examine relationshipsbetween user entries and alimentary instruction sets and/orself-fulfillment instruction sets. Lazy learning process may include anylazy learning process as described above regarding prognostic labellearner 126. Lazy learning processes may be performed by a lazy learningmodule 912 operating on diagnostic engine 104 and/or on anothercomputing device in communication with diagnostic engine 104, which mayinclude any hardware or software module.

Referring now to FIG. 20, an exemplary embodiment of fulfillment module152 is illustrated. Fulfillment module 152 may be designed andconfigured to receive a user entry containing an alimentaryself-fulfillment action. Fulfillment module 152 may receive a user entrycontaining an alimentary self-fulfillment action from user client device156 and/or through client interface module 154. Self-fulfillment actionmay include a description, photograph, selection, choice, and the likedescribing an action a user engaged in to self-fulfill alimentaryinstruction set 144. Action may include any steps, effort, and/or tasksthat a user engage in to self-fulfill alimentary instruction set. Actionmay include for example, making a grocery list, shopping forsupplements, preparing a meal kit, grabbing take out at a restaurant,purchasing a take away meal at a grocery store or meal delivery kitchenand the like. In an embodiment, self-fulfillment action may include anaction as recommended by self-fulfillment instruction set and/or berelated to an action as recommended by self-fulfillment instruction set.For example, self-fulfillment instruction set may recommend an actionsuch as cooking a recipe containing wild salmon and broccoli rabe.Self-fulfillment action may include an action user took such aspurchasing wild salmon at a grocery store or ordering a takeout mealthat contained wild salmon and broccoli rabe.

With continued reference to FIG. 20, fulfillment module 152 may containself-fulfillment database 2000. Self-fulfillment database 2000 maycontain different database tables as described below in more detail inFIG. 21, that user entry containing an alimentary self-fulfillmentaction may be matched with to discover how user's behaviors arecontributing to or hurting a user's vibrant constitution by affectingprognostic label and/or ameliorative process label. For example, a userwith a prognostic label such as Lyme Disease may receive an ameliorativeprocess label that recommends eating a grain free diet to reduceinflammation and reduce bacterial burden in user's body. User entriesdescribing self-fulfillment actions over a period of time may then bematched against database tables located within self-fulfillment database2000 to examine how user's actions have contributed to grain free diet,vibrant constitution, health goals, and/or nutrient density scores.

With continued reference to FIG. 20, fulfillment module 152 may containmatching database 2004. Matching database 2004 may include differentdatabase tables as described below in more detail in FIG. 22. Userentries containing alimentary self-fulfillment action may be matchedutilizing matching database 20004 and/or self-fulfillment database 2000.User entries may be received by fulfillment module 152 as either textualentries such as a description of what a user consumed or purchased,graphical entries such as an upload of a meal user ate at a restaurant,and/or by user selection whereby user may select some type ofself-fulfillment action from a predetermined list or chart. In anembodiment, user may select a self-fulfillment action from a list, suchas one containing actions and/or recommendations from self-fulfillmentinstruction set. For example, self-fulfillment instruction set maycontain a list of 3 options such as a new recipe user could cook, arecommended meal a user could consume, or a grocery store where a usercould purchase groceries at. User may then select which of those 3options user performed if any. In an embodiment, user may providecomments or edit selections such as if instead of consuming salmon andbroccoli rabe as recommended by self-fulfillment instruction set 146,user instead consumed salmon and spinach. User entry may then be matchedagainst a table contained within self-fulfillment database 2000 toexamine how user entry may affect user's vibrant constitution. Forexample, user entry containing repetitive self-fulfillment actions suchas consuming fried foods may negatively affect user's vibrantconstitution. User entry containing a one-time self-fulfillment actionof consuming fried food may not have such an impact on one's vibrantconstitution. Fulfillment module 152 may utilize matching to compareuser entry containing an alimentary self-fulfillment action to at leastan alimentary instruction set. For example, an alimentaryself-fulfillment action containing a list of meals user consumed in oneday may be matched against alimentary instruction set to determine ifuser consumed recommended nutrients or supplements as provided for byalimentary instruction set. For example, a user entry containing a homecooked meal that contained miso cod over buckwheat with a side salad maybe matched against alimentary instruction set to determine if a userentry contains recommended nutrients such as for example an increase indietary magnesium intake. In yet another non-limiting example, userentry such as a grocery list of purchased groceries from an onlinegrocery store may be matched against alimentary instruction set todetermine if user's purchases will fulfill recommended nutrient anddietary recommendations contained within alimentary instruction set. Inan embodiment, alimentary instruction set may be modified as a functionof user entry. For example, an alimentary instruction set may be updatedto contain new nutrients or recommended lower amounts if a user consumeswhat is recommended and subsequent biological samples reflect restoredlevels within normal ranges. In yet another non-limiting example,alimentary instruction set may be updated as a function of user entrysuch as for example, in the winter time when user needs to supplementwith higher dosages of vitamin D due to less sun exposure.

With continued reference to FIG. 20, fulfillment module 152 may usemachine-learning such as by self-fulfillment learner 148 to utilize userentries in a feedback mechanism to provide subsequent an alimentaryinstruction set 144, self-fulfillment instruction set 146, and/orprovide captured data to plan generator module 138. Fulfillment module152 may utilize supervised and/or unsupervised machine-learningprocesses as described above in reference to FIG. 1 and FIG. 18.Fulfillment module 152 mat utilize lazy learning processes as describedabove in reference to FIG. 1 and FIG. 18.

Referring now to FIG. 21, an exemplary embodiment of fulfillmentdatabase 2000 is illustrated. Self-fulfillment database 2000 may beimplemented as any database and/or datastore suitable for use asdescribed above. Self-fulfillment database 2000 may contain informationexamining how user's self-fulfillment selections as transmitted tocomputing device 102 and processed by fulfillment module 152 haveaffected a user's ability to achieve and/or maintain vibrantconstitution. One or more database tables in self-fulfillment database2000 may include, weight loss table 2104; weight loss may includeinformation describing how user's self-fulfillment options andselections have attributed to weight loss if any over a specific periodof time. One or more database tables in self-fulfillment database 2000may include calorie count table 2108, calorie count may includeinformation describing how user's self-fulfillment options andselections have attributed to certain calorie requirements such as thoserecommended by an informed advisor. One or more database tables inself-fulfillment database 2000 may include nutrient density score table2112, nutrient density score may include information describing howuser's self-fulfillment options and selections have led to nutrientdense selections such as for example the nutrient density score ofconsuming a home cooked meal with little oil versus a friend chickensandwich from a fast food restaurant. One or more database tables inself-fulfillment database 2000 may include health maintenance table2116, health maintenance may include information describing how user'sself-fulfillment options and selections have aided a user in maintaininguser's health. User's health may include maintaining a certain status orlevel of health, such as for example staying free of any diagnosedmedical conditions or keeping a disease state such as Ulcerative Colitisin remission without any flares. One or more database tables inself-fulfillment database 2000 may include health goal table 2120, whichmay include information describing how user's self-fulfillment optionsand selections helped or hurt a user in achieving a particular healthgoal. Health goal may include any goal a user may set as it relates touser's health, such as for example, cooking three meals each week athome or ordering low carbohydrate meals at restaurants. One or moredatabase tables in self-fulfillment database 2000 may include vibrantconstitution database 2124, which may include information describing howuser's self-fulfillment options and selections have attributed orhindered a user in achieving and/or maintaining vibrant constitution.One or more database tables in self-fulfillment database 2000 mayinclude medically supervised diet table 2128, which may includeinformation describing how user's self-fulfillment options andselections have attributed to or hindered a user in adhering to amedically supervised diet. Medically supervised diet may include a dietdesigned for weight loss such as one prescribed by a functional medicinedoctor or functional nutritionist, or a medically supervised diet mayinclude a diet utilized to treat or maintain a medical condition toachieve remission such as a ketogenic diet for epilepsy, a gluten freediet for hypothyroidism or the Wahls protocol for multiple sclerosis.One or more database tables in self-fulfillment database 2000 mayinclude miscellaneous table 2132, which may contain any otherinformation relating a user's self-fulfillment options and selections toachieving vibrant constitutional state.

Referring now to FIG. 22, an exemplary embodiment of matching database2004 is illustrated. Matching database 2004 may be implemented as anydatabase and/or datastore suitable for use as described above. Matchingdatabase 2004 may include one or more tables containing one or morecategories of user entries that may be matched against informationcontained within self-fulfillment database 2000 such as by fulfillmentmodule 152. One or more database tables in matching database 2004 mayinclude textual input table 2200, which may include user entriescontaining text such as a word or string of words, description, orparagraph describing user's alimentary self-fulfillment actions. One ormore database tables in matching database 2004 may include graphicaltable 2204, which may include user entries containing graphics such aspictures, images, and/or graphical representations describing user'salimentary self-fulfillment actions. One or more database tables inmatching database 2004 may include user selection table 2208, which mayinclude user entries that a user has selected from a list or drop-downmenu.

Referring now to FIG. 23, an exemplary embodiment of an advisory module158 is illustrated. Advisory module 158 may be configured to generate anadvisor instruction set 1600 as a function of the diagnostic output.Advisory instruction set 2300 may contain any element suitable forinclusion in comprehensive instruction set 140; advisory instruction set2300 and/or any element thereof may be generated using any processsuitable for generation of comprehensive instruction set 140. Advisoryinstruction set 2300 may include one or more specialized instructions1504; specialized instructions, as used herein, are instructions thecontents of which are selected for display to a particular informedadvisor. Selection of instructions for a particular informed advisor maybe obtained, without limitation, from information concerning theparticular informed advisor, which may be retrieved from a user database1036 or the like. As a non-limiting example, where an informed advisoris a doctor, specialized instruction 2304 may include data frombiological extraction as described above; specialized instruction mayinclude one or more medical records of user, which may, as anon-limiting example, be downloaded or otherwise received from anexternal database containing medical records and/or a database (notshown) operating on a computing device 102. As a further non-limitingexample medical data relevant to fitness, such as orthopedic reports,may be provided to an informed advisor whose role is as a fitnessinstructor, coach, or the like.

In an embodiment, and continuing to refer to FIG. 23, advisory module158 may be configured to receive at least an advisory input from theadvisor client device 160. At least an advisory input may include anyinformation provided by an informed advisor via advisor client device160. Advisory input may include medical information and/or advice.Advisory input may include user data, including user habits,preferences, religious affiliations, constitutional restrictions, or thelike. Advisory input may include spiritual and/or religious advice.Advisory input may include user-specific diagnostic information.Advisory input may be provided to user client device 156; alternativelyor additionally, advisory input may be fed back into system 100,including without limitation insertion into user database 1036,inclusion in or use to update diagnostic engine 104, for instance byaugmenting machine-learning models and/or modifying machine-learningoutputs via a lazy-learning protocol or the like as described above.

With continued reference to FIG. 23, advisory module 158 may include anartificial intelligence advisor 2308 configured to perform a usertextual conversation with the user client device 156. Artificialintelligence advisor 2308 may provide output to advisor client device160 and/or user client device 156. Artificial intelligence advisor 2308may receive inputs from advisor client device 160 and/or user clientdevice 156. Inputs and/or outputs may be exchanged using messagingservices and/or protocols, including without limitation any instantmessaging protocols. Persons skilled in the art, up reviewing theentirety of this disclosure, will be aware of a multiplicity ofcommunication protocols that may be employed to exchange text messagesas described herein. Text messages may be provided in textual formand/or as audio files using, without limitation, speech-to-text and/ortext-to-speech algorithms.

With continued reference to FIG. 23, advisory module 158 may output,with advisory output, a textual entry field 2312. Textual entry field2312 may include a searchable input field that allows entry of a searchterm such as a word or phrase to be entered by a user such as aninformed advisor. In an embodiment, textual entry field 2312 may allowfor entry of a search term to be matched with labels contained withinthe at least at diagnostic output. For example, an informed advisor suchas a medical professional may enter into a search term a results of afasting glucose test after receiving at least a diagnostic output ofdiabetes. In such an instance, user such as an informed advisor may beable to search multiple results such as fasting glucose test levelsrecorded over a certain period of time such as several years and/ormonths. In yet another non-limiting example, an informed advisor such asa fitness professional may search for user's most recent exercise logand/or nutrition records. In yet another non-limiting example, aninformed advisor such as a nurse practitioner may enter information intotextual entry field 2312 to search for information pertaining to user'smedication history after receiving at least a diagnostic output of acutekidney injury. In an embodiment, textual entry field 2312 may allow auser such as an informed advisor to navigate different areas of advisoryoutput. For example, an informed advisor may utilize textual entry field2312 to navigate to different locations such as a table of contents, andor sections organized into different categories as described in moredetail below.

With continued reference to FIG. 23, advisory module 158 may include inan advisory output a category field 2316. Category field 2316 mayinclude a textual field that contains advisory output organized intocategories. Category, as used herein, is any breakdown of advisoryoutput by shared characteristics. Categories may include for example,breakdown by informed advisor type. For example, informed advisors maybe categorized into categories of expertise such as spiritualprofessionals, nutrition professionals, fitness professionals and thelike. Categories may include sub-categories of specialties such as forexample functional medicine informed advisors may be organized intosub-categories based on body system they may be treating. This couldinclude sub-categories such as dermatology specialists, Genito-urologyspecialists, gastroenterology specialists, neurology specialists and thelike. Categories may include a breakdown by time such as chronologicalorder and/or reverse chronological order. Categories may be modifiedand/or organized into test results such as for example all completeblood counts that a user has ever had performed may be located in onecategory, and all CT scans that a user has had performed may be locatedin another category. Categories may include a breakdown by relevance,such as highly relevant test results and/or test results that areoutside normal limits.

With continued reference to FIG. 23, advisory module 158 may include inan advisory output a relevance field 2320. Relevance field 2320 as usedherein is a textual field that contains advisory output informationlabeled as being relevant. Relevance, as used herein, is any informationcontained within advisory output that is closely connected and/orrelated to diagnostic output. Relevance may include information thatwould be of interest to a particular category of informed advisor. Forexample, an informed advisor such as an ophthalmologist may deeminformation contained within at least an advisory output such as ameasurement of a user's intra-ocular pressure to be of relevance, whilean advisory output containing information summarizing a user's lastappointment with a podiatrist to not be of relevance. In yet anothernon-limiting example, an informed advisor such as a fitness professionalmay deem information contained within an advisory output such as asummary of a user's last appointment with an orthopedic doctor to berelevant while a summary of a user's last colonoscopy may not berelevant. In an embodiment, relevance may be viewed on a continuum.Information contained within at least an advisory output that directlyrelates to an informed advisor and is of high probative value to aninformed advisor may be highly relevant. For example, a nutritionist maydeem a journal of a user's eating habits as highly relevant. In yetanother non-limiting example, a spiritual professional may deem asummary of a user's church patterns as highly relevant. Information thatis related to an informed advisor but does not directly affect aninformed advisor may be moderately relevant. For example, adermatologist may deem information pertaining to a user's last physicalexam with an internal medicine doctor to be moderately relevant. In yetanother non-limiting example, an endocrinologist may deem informationpertaining to a user's last appointment with a podiatrist to bemoderately relevant for a user diagnosed with diabetes. Information thatis not related to an informed advisor and does not affect an informedadvisor may be of low relevance. For example, a trauma surgeon may deeminformation about a user's last dental cleaning to be of low relevance.In yet another non-limiting example, a cardiologist may deem informationabout a user's last bone density scan to be of low relevance. In anembodiment, user such as informed advisor may use textual entry field2312 to navigate advisory output to find information that is relevant.In an embodiment, information contained within at least an advisoryoutput may be marked as relevant such as by another informed advisor.For example, a functional medicine doctor may mark an elevated fastingblood glucose level as relevant before transmitting such a result to anutrition professional.

In an embodiment, and still referring to FIG. 23, a relevance field 2320may include an image, link, or other visual element that an informedadvisor may select or otherwise interact with to expand or contract aportion of advisory output; for instance, relevance field 2320 mayinclude a symbol next to or on a section heading that can cause acorresponding section of text to display when activated a first time anddisappear when activated a second time. As a result, an informed advisormay be presented initially with some text visible and other text notvisible; initial presentation may hide all text but section headers.Alternatively or additionally, where informed advisor belongs to aparticular category of informed advisor and/or has a profile in, forinstance, advisory database 2324 indicating categories of interest tothe informed advisor, sections of text and/or images related to suchcategories may initially display while other sections do not displayunless a relevance field 2320 corresponding to such sections is selectedby the informed advisor.

With continued reference to FIG. 23, advisory module 158 containsadvisory database 2324. Advisory database 2324 may be implemented as anydatabase and/or datastore suitable for use as an advisory database. Anexemplary embodiment of an advisory database 2324 is provided below inFIG. 25.

Referring now to FIG. 24, an exemplary embodiment of an artificialintelligence advisor 2308 is illustrated. Artificial intelligenceadvisor 2308 may include a user communication learner 2400. Usercommunication learner 2400 may be any form of machine-learning learneras described above, implementing any form of language processing and/ormachine learning. In an embodiment, user communication learner 2400 mayinclude a general learner 2404; general learner 2404 may be a learnerthat derives relationships between user inputs and correct outputs usinga training set that includes, without limitation, a corpus of previousconversations. Corpus of previous conversations may be logged by acomputing device 102 as conversations take place; user feedback, and/orone or more functions indicating degree of success of a conversation maybe used to differentiate between positive input-output pairs to use fortraining and negative input-output pairs not to use for training.Outputs may include textual strings and/or outputs from any databases,modules, and/or learners as described in this disclosure, includingwithout limitation prognostic labels, prognostic descriptors,ameliorative labels, ameliorative descriptors, user information, or thelike; for instance, general learner 2404 may determine that some inputsoptimally map to textual response outputs, while other inputs map tooutputs created by retrieval of module and/or database outputs, such asretrieval of prognostic descriptors, ameliorative descriptors, or thelike. User communication learner may include a user-specific learner2408, which may generate one or more modules that learn input-outputpairs pertaining to communication with a particular user; a userspecific learner 1708 may initially use input-output pairs establishedby general learner 2404 and may modify such pairs to match optimalconversation with the particular user by iteratively minimizing an errorfunction.

Still referring to FIG. 24, general learner 2404 and/or user-specificlearner 2408 may initialize, prior to training, using one or more recordretrieved from a default response database 2412. Default responsedatabase 2412 may link inputs to outputs according to initialrelationships entered by users, including without limitation experts asdescribed above, and/or as created by a previous instance or version ofgeneral learner 2404 and/or user-specific learner 2408. Default responsedatabase 2412 may periodically be updated with information from newlygenerated instances of general learner 2404 and/or user-specific learner2408. Inputs received by artificial intelligence advisor 2308 may bemapped to canonical and/or representative inputs by synonym detection asperformed, for instance, by a language processing module 114; languageprocessing module 114 may be involved in textual analysis and/orgeneration of text at any other point in machine-learning and/orcommunication processes undergone by artificial intelligence advisor2308.

Referring now to FIG. 25, an exemplary embodiment of advisory database2324 is illustrated. One or more database tables in advisory database2324 may link to data surrounding an informed advisor. Advisory database2324 may include one or more database tables categorized by expertise ofinformed advisor. One or more database tables in advisory database 2324may include, without limitation, an artificial intelligence informedadvisors table 2504, which may contain any and all informationpertaining to artificial intelligence informed advisors. One or moredatabase tables in advisory database 2324 may include, withoutlimitation, a spiritual professional informed advisors table 2508, whichmay contain any and all information pertaining to spiritual professionalinformed advisors. Spiritual professional informed advisors may includespiritual professionals who may participate in cultivating spiritualitythrough exercise of practices such as prayer, meditation, breath work,energy work, and the like. One or more database tables in advisorydatabase 2324 may include, without limitation, a nutrition professionalinformed advisors table 2512, which may include any and all informationpertaining to nutritional informed advisors. Nutritional informedadvisors may include dietitians, chefs, and nutritionists who may offerexpertise around a user's diet and nutrition state and supplementation.One or more database tables in advisory database 2324 may include,without limitation a fitness professional informed advisors table 2516,which may include any and all information pertaining to fitnessprofessional informed advisors. Fitness professional informed advisorsmay examine the fitness state of a user and may include personaltrainers, coaches, group exercise instructors, and the like. One or moredatabase tables in advisory database 2324 may include, withoutlimitation a functional medicine informed advisors table 2520, which mayinclude any and all information pertaining to functional medicineinformed advisors. Functional medicine informed advisors may includedoctors, nurses, physician assistants, nurse practitioners and othermembers of the health care team. One or more database tables in advisorydatabase 2324 may include, without limitation a friends and familyinformed advisors table 2524, which may include any and all informationpertaining to friends and family informed advisors. Friends and familyinformed advisors may include friends and family members of a user whomay create a positive community of support for a user. One or moredatabase tables in advisory database 2324 may include, withoutlimitation an electronic behavior coach informed advisor table 2528,which may include any and all information pertaining to electronicbehavior coach informed advisors. Electronic behavior coach informedadvisors may assist a user in achieving certain results such asmodifying behaviors to achieve a result such as assisting in additionrecovery and/or changing a user's eating habits to lose weight. One ormore database tables in advisory database 2324 may include withoutlimitation a miscellaneous informed advisor table 2532, which mayinclude any and all information pertaining to miscellaneous informedadvisors. Miscellaneous informed advisors may include any informedadvisors who do not fit into one of the categories such as for exampleinsurance coverage informed advisors. Miscellaneous informed advisortable 2532 may also contain miscellaneous information pertaining toinformed advisors such as a user's preference for informed advisors in acertain geographical location and/or other preferences for informedadvisors.

Referring now to FIG. 26, an exemplary embodiment of a default responsedatabase 2412 is illustrated. Default response database 2412 may beimplemented as any database and/or datastore suitable for use asdescribed above. One or more database tables in default responsedatabase 2412 may include, without limitation, an input/output table2600, which may link default inputs to default outputs. Default responsedatabase 2412 may include a user table 2604, which may, for instance,map users and/or a user client device 180 to particular user-specificlearners and/or past conversations. Default response database 2412 mayinclude a user preference table 2608 listing preferred modes of address,turns of phrase, or other user-specific communication preferences.Default response database 2412 may include a general preference table2612, which may track, for instance, output-input pairings associatedwith greater degrees of user satisfaction.

Referring again to FIG. 24, artificial intelligence advisor may includea consultation initiator 2416 configured to detect a consultation eventin a user textual conversation and initiate a consultation with aninformed advisor as a function of the consultation event. A consultationevent, as used herein, is a situation where an informed advisor isneeded to address a user's situation or concerns, such as when a usershould be consulting with a doctor regarding an apparent medicalemergency or new condition, or with an advisor who can lend emotionalsupport when particularly distraught. Detection may be performed,without limitation, by matching an input and/or set of inputs to anoutput that constitutes an action of initiating a consultation; such apairing of an input and/or input set may be learned using a machinelearning process, for instance via general learner and/or user specificlearner 2408. In the latter case, information concerning a particularuser's physical or emotional needs or condition may be a part of thetraining set used to generate the input/input set to consultation eventpairing; for instance, a user with a history of heart disease maytrigger consultation events upon any inputs describing shortness ofbreath, chest discomfort, arrhythmia, or the like. Initiation ofconsultation may include transmitting a message to an advisor clientdevice 160 associated with an appropriate informed advisor, such aswithout limitation transmission of information regarding a potentialmedical emergency to a doctor able to assist in treating the emergency.Initiation of consultation may alternatively or additionally includeproviding an output to the user informing the user that a consultationwith an informed advisor, who may be specified by name or role, isadvisable.

Referring now to FIG. 27, an exemplary embodiment of method ofself-fulfillment of an alimentary instruction set based on vibrantconstitutional guidance 2700 is illustrated. At step 2705 at least aserver receives training data. Receiving training data includesreceiving a first training set including a plurality of first dataentries, each first data entry of the plurality of first data entriesincluding at least an element of physiological state data and at least acorrelated first prognostic label. Receiving training data includesreceiving a second training set including a plurality of second dataentries, each second data entry of the plurality of second data entriesincluding at least a second prognostic label and at least a correlatedameliorative process label. Receiving training data may be performed byany of the methodologies a described in FIGS. 1-27. Training data mayinclude any of the training data as described in FIGS. 1-27.

With continued reference to FIG. 27, at step 2710, a computing devicerecords at least a biological extraction from a user. Biologicalextraction may include any of the biological extractions as describedabove in FIGS. 1-27. Recording biological extraction may be performed byany of the methodologies as described above in FIGS. 1027.

With continued reference to FIG. 27, at step 2715 a computing devicegenerates a diagnostic output based on the at least a biologicalextraction and training data. Generating diagnostic output may includeperforming at least a machine-learning algorithm as a function of thetraining data and the at least a biological extraction. Machine-learningalgorithm may include any of the machine-learning algorithms asdescribed above in reference to FIGS. 1027.

With continued reference to FIG. 27, a computing device generates acomprehensive instruction set associated with the user as a function ofthe diagnostic output. Comprehensive instruction set may include any ofthe comprehensive instruction sets as described above in reference toFIGS. 1-27 and may include follow-up suggestions, contextualinformation, future prognostic descriptor, and/or alimentary plandescriptor. Comprehensive instruction set may be generated utilizing anyof the methodologies as described above in reference to FIG. 1-27.

With continued reference to FIG. 27, a computing device generates analimentary instruction set associated with the user as a function of thecomprehensive instruction set. Alimentary instruction set may begenerated upon receiving at least a datum of user data including aconstitutional restriction. Constitutional restriction may include anyof the constitutional restrictions as described above in reference toFIGS. 1-27. Alimentary instruction set may be generated upon receivingat least a datum of user data such as a user preference. In anembodiment, alimentary instruction set may be transmitted to a user.This may be done for example, by transmitting alimentary instruction setto user client device 156. In an embodiment, self-fulfillmentinstruction set may be generated utilizing a loss function of userspecific variables and minimizing the loss function. This may be done ona variable by variable basis. In an embodiment, after a user hasimplemented one step of the self-fulfillment instruction set, the lossfunction may be re-run to update the self-fulfillment instruction set.In an embodiment, the self-fulfillment instruction set may be generatedas a function of user geolocation. In an embodiment, self-fulfillmentinstruction set may be transmitted to a user. Self-fulfillmentinstruction set may include any of the self-fulfillment instruction setsas described above in reference to FIGS. 1-27.

With continued reference to FIG. 27, at step 2715, a computing device102 generates a self-fulfillment instruction set identifying aself-fulfillment action. Self-fulfillment instruction set includes anyof the self-fulfillment instruction sets as described above in moredetail. Self-fulfillment instruction set is generated utilizing any ofthe methodologies as described above in more detail. Computing device102 generates self-fulfillment instruction set utilizing a fulfillmentmachine-learning model. Computing device 102 receives user training datacontaining previous user alimentary instruction sets and a plurality ofcorrelated self-fulfillment instruction sets. Computing device 102generates a self-fulfillment instruction set utilizing user trainingdata and fulfillment machine-learning model. Fulfillmentmachine-learning model includes any of the machine-learning models asdescribed above in more detail. Computing device 102 generates aself-fulfillment instruction set based on a user geolocation. Computingdevice 102 may generate a self-fulfillment instruction set to identify aself-fulfillment action located with a user geolocation. For instanceand without limitation, computing device 102 may generate aself-fulfillment instruction set to identify a self-fulfillment actionlocated within a user geolocation. For example, for a user with ageolocation in Miami, Fla., computing device 102 may generate aself-fulfillment instruction set that contains a list of groceries thatcan be purchased online and shipped to the user in Miami Florida. In yetanother non-limiting example, for a user with a geolocation in DenverColorado, computing device 102 may generate a self-fulfillmentinstruction set that contains a meal a user can order from a restaurantlocated in Denver Colorado.

With continued reference to FIG. 27, at step 2725 a computing devicereceives a user entry containing an alimentary self-fulfillment action.Alimentary self-fulfillment action may include any of the alimentaryself-fulfillment actions as described above in reference to FIGS. 1-27.Reception of self-fulfillment action may be utilized to track a userlocation and match alimentary instruction sets and/or self-fulfillmentinstruction sets to a user. For example, if a user decides to takeaction regarding a self-fulfillment instruction set such as followingdirections to a specific grocery store, then user entry may be analyzedand matched to inquire if user actually purchased items relating toalimentary instruction set. In an embodiment, a user may pull up ascreen that may allow a user to enter purchased ingredients or scanbarcodes pertaining to specific ingredients. Alimentary self-fulfillmentaction may be received using any of the methodologies as describedherein. In an embodiment, user entry containing alimentaryself-fulfillment action may be matched by fulfillment module 152.Matching may include any of the matching methodologies as describedabove in reference to FIGS. 20-22. Matching may include matching a userentry containing an alimentary self-fulfillment action to at least analimentary instruction set and/or to at least a self-fulfillmentinstruction set. In an embodiment, self-fulfillment action may includetextual entries, graphics such as photographs, and/or user selectionfrom a drop-down menu and may be received using any methodologies asdescribed herein. In an embodiment, alimentary instruction set may bemodified as a function of user entry. For example, an alimentaryinstruction set that recommends a user to increase intake of iodine richfoods such as kelp and seaweed for a sluggish thyroid may be modifiedafter a user has entered user entries that contain consumption of suchfoods. In yet another non-limiting example, alimentary instruction setthat recommends a user to increase intake of iron rich foods for a userwith anemia may be updated to recommend consumption of iron rich foodswith vitamin c foods to better increase absorption of iron if ironlevels do not increase after user entries contain descriptions of foodsand/or meals consumed that contain iron rich ingredients. In yet anothernon-limiting example, alimentary instruction set may be modified onceuser's blood stores of iron are replete.

With continued reference to FIG. 27, computing device 102 is configuredto receive a user entry containing a photograph of a self-fulfillmentaction. Computing device 102 may receive a wireless transmission from auser client device, containing a photograph of a self-fulfillmentaction. For example, computing device 102 may receive a photograph of ameal a user ordered from a restaurant or a photograph of produce userpurchased at a farm stand. In an embodiment, a photograph may includereceipt data. Receipt data includes any of the receipt data as describedabove. For example, receipt data may include a receipt of an electronicgrocery order or a receipt of a meal delivery service. Computing device102 may classify a photograph of a self-fulfillment action to aself-fulfillment activity category label utilizing self-fulfillmentclassifier as described above in more detail. For example, computingdevice 102 may classify a photograph of a grocery receipt purchased at afood store to a self-fulfillment activity category label that indicatesin store grocery shopping. In yet another non-limiting example,computing device 102 may classify a photograph of a meal delivery kit toa self-fulfillment activity category label that indicates home cook.Computing device 102 updates a self-fulfillment instruction setutilizing a self-fulfillment activity category label. For instance andwithout limitation, a photograph categorized to in store groceryshopping may be utilized to generate a self-fulfillment instruction setthat contains a self-fulfillment activity for subsequent in storegrocery shopping. In yet another non-limiting example, a photographcategorized to restaurant ordering may be utilized to generate aself-fulfillment instruction set that contains subsequent menu orderingand suggested meal options.

With continued reference to FIG. 27, at step 2730 computing device 102updates a self-fulfillment instruction set utilizing a self-fulfillmentaction. Computing device 102 updates a self-fulfillment instruction setutilizing any of the methods as described herein. Computing device 102may update a self-fulfillment instruction set utilizing user input.Computing device 102 receives a user input from a user client devicecontaining a variable related to fulfillment. Computing device 102 mayreceive a user input utilizing any network methodology as describedherein. Computing device 102 generates a loss function utilizing a userinput and minimizes the loss function. This may be performed utilizingany of the methodology as described above. Computing device 102 mayupdate a self-fulfillment action utilizing a biological extraction thatindicates user digestibility. Computing device 102 records a secondbiological extraction containing an element of user physiological datacontaining an indication of user digestibility. An indication of userdigestibility includes any of the indicators as described above in moredetail in reference to FIG. 1. Computing device 102 updates aself-fulfillment instruction set utilizing an indication of userdigestibility. For instance and without limitation, an indication ofuser digestibility that indicates a user has low intracellular levels ofVitamin C may be utilized by computing device 102 to update aself-fulfillment instruction set to recommend self-fulfillment actionsthat contain high amounts of Vitamin C. In yet another non-limitingexample, an indication of user digestibility that indicates a user haslow blood levels of iron may be utilized by computing device 102 toupdate a self-fulfillment instruction set to recommend self-fulfillmentactions that contain slow cooked meats and fulfillment actions that

With continued reference to FIG. 1, computing device 102 is configuredto evaluate a nourishment allotment of a completed alimentaryself-fulfillment action and update a self-fulfillment instruction setutilizing a nourishment outcome. A nourishment allotment, as used inthis disclosure, is data describing any nutritional content of any fooditem. A nutritional content may indicate if a food item is a vegetableor fruit, or if a food item contains nutrients such as iron or fiber. Anutritional content may indicate if a food item is something that has anegative effect on a human body such as fried foods or foods high inadded sugars. A nutritional content may indicate if a food item issomething that has a positive effect on a human body such as cruciferousvegetables or foods high in monounsaturated fats such as avocado,almond, or peanuts. For example, a nourishment allotment for a meal auser purchased at a restaurant may indicate that the meal contained wildsalmon high in protein and omega three fatty acids, and broccoli sautéedin avocado oil and high in Vitamin K, potassium, and indole 3 carbinol.Computing device 102 may compare a nourishment allotment of a completedalimentary self-fulfillment action to a nourishment requirementcontained within an alimentary instruction set. A nourishmentrequirement, as used in this disclosure, is data describing nutritionalneeds of a user. A nourishment requirement may be generated based on auser's alimentary instruction set. For instance and without limitation,a nourishment requirement may indicate that a user has high blood sugarand needs to consume a Mediterranean diet. In yet another non-limitingexample, a nourishment requirement may indicate that a user has anemiaand needs to consume twenty five milligrams per day of iron. Computingdevice 102 compares a nourishment allotment to a nourishment requirementto determine a nourishment outcome. A nourishment outcome, as used inthis disclosure, is data describing any nutritional status of a user. Anutritional status may indicate if a user has completed a user'snourishment requirement and if user needs an updated nourishmentallotment. For example, computing device 102 may determine that a userwith hypothyroidism has not consumed enough iodine from food sourcesbased on a user's nourishment allotment as compared to a user'snourishment requirement to consume 500 micrograms per day of iodine. Insuch an instance, computing device 102 may determine a nourishmentoutcome that indicates the user needs to consume additional iodine.Computing device 102 may update a self-fulfillment instruction set toaccount for the additional iodine and may recommend self-fulfillmentactions that contain groceries, meals, and/or food items that containadditional iodine.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 28 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 2800 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 2800 includes a processor 2804 and a memory2808 that communicate with each other, and with other components, via abus 2812. Bus 2812 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Memory 2808 may include various components (e.g., machine-readablemedia) including, but not limited to, a random-access memory component,a read only component, and any combinations thereof. In one example, abasic input/output system 2816 (BIOS), including basic routines thathelp to transfer information between elements within computer system2800, such as during start-up, may be stored in memory 2808. Memory 2808may also include (e.g., stored on one or more machine-readable media)instructions (e.g., software) 2820 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 2808 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 2800 may also include a storage device 2824. Examples ofa storage device (e.g., storage device 2824) include, but are notlimited to, a hard disk drive, a magnetic disk drive, an optical discdrive in combination with an optical medium, a solid-state memorydevice, and any combinations thereof. Storage device 2824 may beconnected to bus 2812 by an appropriate interface (not shown). Exampleinterfaces include, but are not limited to, SCSI, advanced technologyattachment (ATA), serial ATA, universal serial bus (USB), IEEE 1394(FIREWIRE), and any combinations thereof. In one example, storage device2824 (or one or more components thereof) may be removably interfacedwith computer system 2800 (e.g., via an external port connector (notshown)). Particularly, storage device 2824 and an associatedmachine-readable medium 2828 may provide nonvolatile and/or volatilestorage of machine-readable instructions, data structures, programmodules, and/or other data for computer system 2800. In one example,software 2820 may reside, completely or partially, withinmachine-readable medium 2828. In another example, software 2820 mayreside, completely or partially, within processor 2804.

Computer system 2800 may also include an input device 2832. In oneexample, a user of computer system 2800 may enter commands and/or otherinformation into computer system 2800 via input device 2832. Examples ofan input device 2832 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 2832may be interfaced to bus 2812 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 2812, and any combinations thereof. Input device 2832may include a touch screen interface that may be a part of or separatefrom display 2836, discussed further below. Input device 2832 may beutilized as a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 2800 via storage device 2824 (e.g., a removable disk drive, aflash drive, etc.) and/or network interface device 2840. A networkinterface device, such as network interface device 2840, may be utilizedfor connecting computer system 2800 to one or more of a variety ofnetworks, such as network 2844, and one or more remote devices 2848connected thereto. Examples of a network interface device include, butare not limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, and any combination thereof.Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two computing devices, and any combinations thereof. A network,such as network 2844, may employ a wired and/or a wireless mode ofcommunication. In general, any network topology may be used. Information(e.g., data, software 2820, etc.) may be communicated to and/or fromcomputer system 2800 via network interface device 2840.

Computer system 2800 may further include a video display adapter 2852for communicating a displayable image to a display device, such asdisplay device 2836. Examples of a display device include, but are notlimited to, a liquid crystal display (LCD), a cathode ray tube (CRT), aplasma display, a light emitting diode (LED) display, and anycombinations thereof. Display adapter 2852 and display device 2836 maybe utilized in combination with processor 2804 to provide graphicalrepresentations of aspects of the present disclosure. In addition to adisplay device, computer system 2800 may include one or more otherperipheral output devices including, but not limited to, an audiospeaker, a printer, and any combinations thereof. Such peripheral outputdevices may be connected to bus 2812 via a peripheral interface 2856.Examples of a peripheral interface include, but are not limited to, aserial port, a USB connection, a FIREWIRE connection, a parallelconnection, and any combinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. A system for self-fulfillment of an alimentaryinstruction set based on vibrant constitutional guidance, the systemcomprising: a computing device; a diagnostic engine operating on thecomputing device, the diagnostic engine configured to: receive trainingdata, wherein receiving the training data further comprises: receiving afirst training set including a plurality of first data entries, eachfirst data entry of the plurality of first data entries including atleast an element of physiological state data and at least a correlatedfirst prognostic label; and receiving a second training set including aplurality of second data entries, each second data entry of theplurality of second data entries including at least a second prognosticlabel and at least a correlated ameliorative process label; record atleast a biological extraction from a user wherein the at least abiological extraction contains at least an element of physiologicalstate data; and generate a diagnostic output based on the at least abiological extraction and the training data, wherein generating furthercomprises performing at least a machine-learning algorithm as a functionof the training data and the at least a biological extraction; and afulfillment module operating on the at least a server the fulfillmentmodule designed and configured to: generate a self-fulfillmentinstruction set utilizing the diagnostic output wherein theself-fulfillment instruction set identifies a self-fulfillment action;receive at least a user entry containing a completed alimentaryself-fulfillment action; update the self-fulfillment instruction set asa function of the alimentary self-fulfillment action; record at least asecond biological extraction wherein the second biological extractioncontains an element of user physiological data containing an indicationas to user digestibility; and update the self-fulfillment instructionset utilizing the indication as to user digestibility.
 2. The system ofclaim 1, wherein the computing device further comprises: a plangenerator module operating on the computing device, the plan generatormodule configured to generate a comprehensive instruction set associatedwith the user as a function of the diagnostic output; and an alimentaryinstruction set generator module operating on the computing device, thealimentary instruction set generator module designed and configured togenerate at least an alimentary instruction set as a function of thecomprehensive instruction set.
 3. The system of claim 1, wherein thefulfillment module is further configured to: receive user training data,wherein user training data further comprises a plurality of previoususer entries containing previous user alimentary instruction sets, and aplurality of correlated self-fulfillment instruction sets; and generatea self-fulfillment instruction set utilizing the user training data anda first machine-learning model.
 4. The system of claim 1, wherein thefulfillment module is further configured to: receive from a user clientdevice, an element of data describing a user geolocation; and generatethe self-fulfillment instruction set to identify a self-fulfillmentaction located within the user geolocation.
 5. The system of claim 1,wherein the fulfillment module further is further configured to receive,at an image capture device, located on the computing device, a wirelesstransmission from a user client device, containing a photograph of aself-fulfillment action.
 6. The system of claim 5, wherein thefulfillment module is further configured to: input, the user entrycontaining the photograph of the self-fulfillment action, to aself-fulfillment classifier, the self-fulfillment classifier configuredto input the photograph of the self-fulfillment action and output aself-fulfillment activity category label; and update theself-fulfillment instruction set utilizing the self-fulfillment activitycategory label.
 7. The system of claim 5, wherein the photograph of theself-fulfillment action further comprises receipt data.
 8. The system ofclaim 1, wherein the fulfillment module is further configured to:compare a nourishment allotment of the completed alimentaryself-fulfillment action to a nourishment requirement contained within analimentary instruction set to determine a nourishment outcome; andupdate the self-fulfillment instruction set utilizing the nourishmentoutcome.
 9. The system of claim 1, wherein the fulfillment module isfurther configured to: receive a user input from a user client device,wherein the user input contains a variable preference related tofulfillment; generate a loss function utilizing the user input; andminimize the loss function.
 10. A method of self-fulfillment of analimentary instruction set based on vibrant constitutional guidance, themethod comprising: receiving by a computing device training data,wherein receiving the training data further comprises: receiving a firsttraining set including a plurality of first data entries, each firstdata entry of the plurality of first data entries including at least anelement of physiological state data and at least a correlated firstprognostic label; and receiving a second training set including aplurality of second data entries, each second data entry of theplurality of second data entries including at least a second prognosticlabel and at least a correlated ameliorative process label; recording bythe computing device at least a biological extraction from a userwherein the at least a biological extraction contains at least anelement of physiological state data; generating by the computing devicea diagnostic output based on the at least a biological extraction andthe training data, wherein generating further comprises performing atleast a machine-learning algorithm as a function of the training dataand the at least a biological extraction; generating by the computingdevice a self-fulfillment instruction set utilizing the diagnosticoutput wherein the self-fulfillment instruction set identifies aself-fulfillment action; receiving by the computing device at least auser entry containing a completed alimentary self-fulfillment action;updating by the computing device the self-fulfillment instruction set asa function of the alimentary self-fulfillment action, wherein updatingthe self-fulfillment instruction set further comprises: recording atleast a second biological extraction wherein the second biologicalextraction contains an element of user physiological data containing anindication as to user digestibility; and updating the self-fulfillmentinstruction set utilizing the indication as to user digestibility. 11.The method of claim 10, wherein generating the diagnostic output furthercomprises: generating a comprehensive instruction set associated withthe user as a function of the diagnostic output; and generating at leastan alimentary instruction set as a function of the comprehensiveinstruction set.
 12. The method of claim 10, wherein generating theself-fulfillment instruction set further comprises: receiving usertraining data, wherein user training data further comprises a pluralityof previous user entries containing previous user alimentary instructionsets, and a plurality of correlated self-fulfillment instruction sets;and generating a self-fulfillment instruction set utilizing the usertraining data and a first machine-learning model.
 13. The method ofclaim 10, wherein generating the self-fulfillment instruction setfurther comprises: receiving from a user client device an element ofdata describing a user geolocation; and generating the self-fulfillmentinstruction set to identify a self-fulfillment action located within theuser geolocation.
 14. The method of claim 10, wherein receiving the atleast a user entry further comprises receiving at an image capturedevice, located on the computing device, a wireless transmission from auser client device, containing a photograph of a self-fulfillmentaction.
 15. The method of claim 14 further comprising: inputting, theuser entry containing the photograph of the self-fulfillment action, toa self-fulfillment classifier, the self-fulfillment classifierconfigured to input the photograph of the self-fulfillment action andoutput a self-fulfillment activity category label; and updating theself-fulfillment instruction set utilizing the self-fulfillment activitycategory label.
 16. The method of claim 14, wherein receiving thephotograph of the self-fulfillment action further comprises receivingreceipt data.
 17. The method of claim 10, wherein updating theself-fulfillment instruction set further comprises: comparing anourishment allotment of the completed alimentary self-fulfillmentaction to a nourishment requirement contained within an alimentaryinstruction set to determine a nourishment outcome; and updating theself-fulfillment instruction set utilizing the nourishment outcome. 18.The method of claim 10, wherein updating the self-fulfillmentinstruction set further comprises: receiving a user input from a userclient device, wherein the user input contains a variable preferencerelated to fulfillment; generating a loss function utilizing the userinput; and minimizing the loss function.